Detecting cancer risk

ABSTRACT

The present disclosure relates to methods, kits, and systems for assessing the risk of a human subject for developing a cancer, including genetic risk assessment, clinical risk assessment, and combinations of both to improve risk analysis.

RELATED APPLICATIONS

This application is a divisional of U.S. application Ser. No. 15/996,193 filed on Jun. 1, 2018, which claims priority to U.S. provisional application No. 62/514,577 (filed Jun. 2, 2017), 62/529,272 (filed Jul. 6, 2017), and 62/553,249 (filed Sep. 1, 2017), the entire contents of each of which are hereby incorporated by reference.

FIELD OF THE DISCLOSURE

The present disclosure relates to methods, kits, and systems for assessing the risk of a human subject for developing a cancer, including genetic risk assessment, clinical risk assessment, and combinations of both to improve risk analysis.

BACKGROUND OF THE DISCLOSURE

Cancer is a major public health problem, accounting for roughly 25% of all deaths in the United States. American Cancer Society, Facts and Figures 2010. Early detection and treatment of cancer, in particular breast cancer, can significantly improve a patient's chances of long-term survival. Schneble et al., J. CANCER (2014) 5:281-290. For breast cancer, for example, patients and clinicians have several useful tools for early detection, but some of these (e.g., MRI) can be quite costly. There is therefore a compelling need to identify patients at increased risk for disease so that limited detection resources can be focused on these patients.

As a fundamentally genetic disease, a significant proportion of cancer cases have a strong hereditary or familial basis. It has been estimated that 50% of cancer is sporadic and the other half is caused by some heritable factor(s). Moller et al., CANCER EPIDEMIOL. BIOMARKERS PREV. (2016) 25:145-145. In 5-10% of cancers, this heritable factor is a variant in a gene where pathogenic variants have been shown to be of high-to-moderate penetrance (i.e., such variants are independent drivers of a significant increase in disease risk). There is a strong need to decipher the driver(s) in the other 40-45% of cancers attributable to heredity.

BRIEF SUMMARY OF THE DISCLOSURE

The present disclosure relates to specific loci (single nucleotide polymorphisms or “SNPs”) within the genome, and combinations thereof, the detection and analysis of which is useful for assessing the risk of a human subject for developing cancer. This document therefore discloses methods, kits, systems, etc. for quantifying risk for developing cancer.

Accordingly, one aspect of the present disclosure relates to a method for genotyping a subject comprising: analyzing a sample of DNA obtained or derived from the subject to detect the genotype for a plurality of genomic loci, wherein the plurality consists of at most X loci and comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, or more test loci listed in Table 1, and/or for one or more of such test locus a locus in linkage disequilibrium with one or more thereof.

Another aspect of the present disclosure relates to a method for assessing risk for developing cancer comprising:

-   -   (1) analyzing a sample of DNA obtained or derived from a subject         to detect the subject's genotype for a plurality of test genomic         loci comprising at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,         14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65,         70, 75, 80, or more of the loci listed in Table 1, and/or for         one or more of such test genomic locus a locus in linkage         disequilibrium with one or more thereof; and     -   (2) determining (or estimating) the subject's risk for         developing cancer based at least in part on the genotypes         detected in (1).

Another aspect of the present disclosure relates to a method for assessing risk for developing cancer comprising:

-   -   (1) analyzing a sample of DNA obtained or derived from a subject         to detect the subject's genotype for a plurality of test genomic         loci comprising at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,         14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65,         70, 75, 80, or more of the loci listed in Table 1, and/or for         one or more of such test genomic locus a locus in linkage         disequilibrium with one or more thereof;     -   (2) evaluating one or more clinical characteristics of the         subject; and     -   (3) determining (or estimating) the subject's risk for         developing cancer based at least in part on the genotypes         detected in (1) and the clinical characteristic(s) evaluated in         (2).

Another aspect of the present disclosure relates to a method for assessing risk for developing cancer comprising:

-   -   (1) analyzing a sample of DNA obtained or derived from a subject         to detect the subject's genotype for a plurality of test genomic         loci comprising at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,         14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65,         70, 75, 80, or more of the loci listed in Table 1, and/or for         one or more of such test genomic locus a locus in linkage         disequilibrium with one or more thereof;     -   (2) calculating a test score incorporating the genotypes         detected in (1); and     -   (3)(a) diagnosing a subject for whom the score in (2) exceeds a         reference score as having a test likelihood of developing cancer         that is higher than a reference likelihood of developing cancer;         or     -   (3)(b) diagnosing a subject for whom the score in (2) does not         exceed a reference score as having a test likelihood of         developing cancer that is equal to or lower than a reference         likelihood of developing cancer.

Another aspect of the present disclosure relates to a method for assessing risk for developing cancer comprising:

-   -   (1) analyzing a sample of DNA obtained or derived from a subject         to detect the subject's genotype for a plurality of test genomic         loci comprising at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,         14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65,         70, 75, 80, or more of the loci listed in Table 1, and/or for         one or more of such test genomic locus a locus in linkage         disequilibrium with one or more thereof;     -   (2) evaluating one or more clinical characteristics of the         subject;     -   (3) calculating a test score incorporating the genotypes         detected in (1) and the clinical characteristic(s) evaluated in         (2); and     -   (4)(a) diagnosing a subject for whom the score in (2) exceeds a         reference score as having a test likelihood of developing cancer         that is higher than a reference likelihood of developing cancer;         or     -   (4)(b) diagnosing a subject for whom the score in (2) does not         exceed a reference score as having a test likelihood of         developing cancer that is equal to or lower than a reference         likelihood of developing cancer.

Another aspect of the present disclosure relates to a method of screening for cancer in a subject comprising assessing the risk of the subject for developing cancer as disclosed in any aspect of the present disclosure, and screening for cancer in a subject determined to have a risk for developing breast cancer that is increased (e.g., over general population risk) or is above some threshold (e.g., greater than 15%, 20%, 25%, 30%, 35%, 40%, 50%, 60%, 70%, 80%, 90% or greater risk of developing cancer). For example, screening for breast cancer can involve screening the subject for breast cancer by mammography or MRI, performing (or administering, prescribing, or recommending) either or both of such screenings at some specific age (e.g., 30 years, 40 years, 5 or 10 years before the age of the earliest breast cancer diagnosis in the family) or predetermined interval (e.g., yearly, every two years, every three years, every four years, every five years, etc.), enrolling the subject in a screening breast MRIC and mammography program. Screening is often indicated for women with an approximately 20-25% lifetime risk of breast cancer (Saslow et al., 2007). Thus, in some embodiments, a subject with a risk (or a score corresponding to) greater than about 20% lifetime risk is screened as described above, e.g., screened for breast cancer by mammography or MRI, screened at some specific age (e.g., 30 years, 40 years, 5 or 10 years before the age of the earliest breast cancer diagnosis in the family) or predetermined interval (e.g., yearly, every two years, every three years, every four years, every five years, etc.), enrolled in a screening breast MRIC and mammography program.

Another aspect of the present disclosure relates to methods of treating subjects. This may include a method for treating a subject comprising:

-   -   (1) determining the subject's genotype for a plurality of test         genomic loci comprising at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,         12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55,         60, 65, 70, 75, 80, or more of the loci listed in Table 1,         and/or for one or more of such test genomic locus a locus in         linkage disequilibrium with one or more thereof;     -   (2) determining (or estimating) the subject's risk for         developing cancer based at least in part on the genotypes         determined in (1);     -   (3) prescribing or administering one or more treatment         modalities indicated based at least in part on the risk         determined (or estimated) in (2).

This may also include a method for preventing cancer in a subject comprising assessing the risk of the subject for developing cancer as disclosed in any aspect of the present disclosure, and administering an anti-cancer therapy (e.g., medical management that includes some action meant to prevent cancer) to a subject determined to have a risk for developing breast cancer that is increased (e.g., over general population risk) or is above some threshold (e.g., greater than 15%, 20%, 25%, 30%, 35%, 40%, 50%, 60%, 70%, 80%, 90% or greater risk of developing cancer). Pharmacological intervention is often indicated in women with a risk score greater than about 1.66% 5-year risk (Visvanathan et al., 2009). Thus, in some embodiments, a chemopreventative agent (e.g., estrogen receptor therapy, oral contraceptives, etc.) is prescribed or administered to a subject having a risk (or score corresponding to) greater than about 1.66% 5-year risk as determined herein.

This may also include an anti-breast cancer therapy for use in preventing breast cancer in a subject at risk thereof, wherein the risk of the subject for developing cancer is determined as disclosed in any aspect of the present disclosure. In some embodiments, the therapy inhibits estrogen.

Another aspect of the disclosure provides a kit for genotyping a subject comprising: a compartmentalized container; reagents for analyzing a sample of DNA obtained or derived from the subject to detect the genotype for a plurality of genomic loci, wherein the plurality consists of at most X loci and comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, or more test loci listed in Table 1, and/or for one or more of such test locus a locus in linkage disequilibrium with one or more thereof; and instructions for use of the reagents.

Another aspect of the disclosure provides a system for genotyping a subject, comprising: (1) a sample analyzer for analyzing DNA in, extracted from or derived from a sample of the subject to detect the subject's genotype for a plurality of test genomic loci comprising at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, or more of the loci listed in Table 1, and/or for one or more of such test genomic locus a locus in linkage disequilibrium with one or more thereof, wherein the sample analyzer contains (a) the sample, (b) genomic DNA from the sample, (c) transcript RNA from the sample, or (d) DNA derived (e.g., synthesized or amplified) from said genomic DNA; (2) one or more computer programs for performing any one, all, or any combination of the following functions (a) a computer program for querying the subject's genotype data; (b) calculating the subject's risk for developing cancer based at least on the subject's genotype data; (c) calculating a test score based on or derived from the subject's genotype data; (d) comparing the test score to one or more reference scores; (e) concluding whether there is an increased risk of cancer based at least in part on the comparison in (d); and (f) outputting (e.g., displaying) the conclusion in (e).

Another aspect of the present disclosure provides computer program products comprising a computer-usable medium having computer-readable program codes or instructions embodied thereon for enabling a processor to carry out the methods of the disclosure. Generally speaking, the computer-usable medium comprises (1) a computer program for receiving, storing, and/or retrieving a subject's genotype data for a plurality of test genomic loci comprising at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, or more of the loci listed in Table 1, and/or for one or more of such test genomic locus a locus in linkage disequilibrium with one or more thereof; and (2) one or more computer programs for performing any one, all, or any combination of the following functions (a) a computer program for querying the subject's genotype data; (b) calculating the subject's risk for developing cancer based at least on the subject's genotype data; (c) calculating a test score based on or derived from the subject's genotype data; (d) comparing the test score to one or more reference scores; (e) concluding whether there is an increased risk of cancer based at least in part on the comparison in (d); and (f) outputting (e.g., displaying) the conclusion in (e). In some embodiments this program for outputting the conclusion may comprise a computer program for informing a health care professional of the conclusion.

Another aspect of the present disclosure relates to a computer implemented method for assessing the risk of a subject for developing cancer, the method operable in a computing system comprising a processor and a memory, the method comprising:

-   -   (1) receiving genetic risk data for the subject, wherein the         genetic risk data was obtained by detecting, in a sample derived         from the subject, the genotype for a plurality of test genomic         loci comprising at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,         14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65,         70, 75, 80, or more of the loci listed in Table 1, and/or for         one or more of such test genomic locus a locus in linkage         disequilibrium with one or more thereof;     -   (2) optionally processing the data to combine the genetic risk         data with clinical risk data to obtain the risk of the subject         for developing cancer;     -   (3) outputting the risk of the subject for developing cancer.         In some embodiments, the clinical risk data and/or genetic risk         data for the subject are received from a user interface coupled         to the computing system. In some embodiments, the clinical risk         data and/or genetic risk data for the subject are received from         a remote device across a wireless communications network. In         some embodiments, outputting comprises outputting information to         a user interface coupled to the computing system. In some         embodiments, outputting comprises transmitting information to a         remote device across a wireless communications network.

Another aspect of the present disclosure relates to a system configured to perform the disclosed methods. This may include a system for assessing the risk of a subject for developing cancer comprising:

-   -   (a) system instructions for performing a genetic risk assessment         of the subject according to any of the method aspects of the         present disclosure; and     -   (b) optionally system instructions for performing a clinical         risk assessment of the subject;     -   (c) optionally system instructions for combining the clinical         risk assessment with the genetic risk assessment to obtain the         risk of a human female subject for developing breast cancer.

As will be apparent, at least some features of the methods, kits and systems can be used together in combination. For example, systems for identifying correlations between breast cancer susceptibility and polymorphisms can be used for practicing the methods herein. Kits can be used for practicing the methods herein. Thus, described features of the systems, methods and kits can be applied to the different systems, methods and kits herein.

The present disclosure includes numerous embodiments of each of the preceding aspects, including but not limited to:

-   -   (a) Embodiments further comprising assessing clinical         characteristics of the subject and combining this assessment         with genotyping to derive an improved assessment of cancer risk.     -   (b) Embodiments where the subject lacks or has been determined         to not harbor a pathogenic variant in any high penetrance cancer         predisposition gene.     -   (c) Embodiments where determining (or estimating) the subject's         risk for developing cancer comprises determining (or estimating)         the subject's percent probability of developing cancer within         one or more specific periods of time. In some such embodiments,         the period(s) of time may be the next five years; a plurality of         five-, ten-, 15-, or 20-year intervals; the remainder of the         subject's expected lifetime; any time before a specific age         (e.g., 50, 55, 60, 65, 70, 75, 80, 85, 90 years of age); etc.     -   (d) Embodiments where determining (or estimating) the subject's         risk for developing cancer comprises determining the subject's         risk for developing cancer attributable or allocable to the         detected or determined genotypes.     -   (e) Embodiments further comprising performing a clinical risk         assessment. In some such embodiments the clinical risk         assessment comprises obtaining information from the subject on         one or more of the following: medical history of breast cancer,         ductal carcinoma or lobular carcinoma, age, age of first         menstrual period, age at which she first gave birth, family         history of breast cancer, results of previous breast biopsies,         breast density, and race/ethnicity.     -   (f) Embodiments where determining (or estimating) the subject's         risk for developing cancer comprises combining the subject's         risk for developing cancer attributable or allocable to the         detected genotypes with a clinical risk assessment (e.g., as         in (e) above) or other risk factors, calculations, scores, or         estimations to yield a composite risk score or estimation. In         some such embodiments the clinical risk assessment or other risk         factors, calculations, scores or estimations include any of the         following: a Gail Model, a Claus Model, Claus Tables, BOADICEA,         a Jonker Model, a Claus Extended Formula, a Tyrer-Cuzick Model,         BRCAPRO, or a Manchester Scoring System. In some such         embodiments the other score or estimation is a score or risk         estimation derived from a Tyrer-Cuzick Model (e.g., Tyrer-Cuzick         version 7 or version 8).     -   (g) Embodiments where determining (or estimating) the subject's         risk for developing cancer comprises calculating a test score         incorporating or derived from the genotypes detected in (1). In         some such embodiments, determining in (2) comprises comparing         the test score to a reference score.     -   (h) Embodiments where risk of developing cancer is determined         (or estimated) for breast cancer.     -   (i) Embodiments where the subject's race is Caucasian.     -   (j) Embodiments where, the subject has not had any previous         cancer. In some embodiments the subject has not had breast         cancer, lobular carcinoma or ductal carcinoma.     -   (k) Embodiments where a second locus is in linkage         disequilibrium with a first locus in Table 1 if such second         locus has linkage disequilibrium above 0.9 or 1.     -   (l) Embodiments where the sample is, or is derived from, a         bodily fluid. In some embodiments, the sample is, or is derived         from, known germline tissue (e.g., nucleated blood cells,         fibroblasts, etc.).

The foregoing and other advantages and features of the disclosure, and the manner in which the same are accomplished, will become more readily apparent upon consideration of the following detailed description of the disclosure taken in conjunction with the accompanying examples and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an example of test locus (SNP) genotypes coded as the number of the effect (risk modifying) alleles detected at such test locus.

FIG. 2 shows the 82 most informative test loci (SNPs) that yielded the maximum of the likelihood ratios.

FIG. 3 shows the distribution of odds ratios associated with RRS (test score) in unaffected women from the validation cohort.

FIG. 4 is an illustration of an example of a system useful in certain aspects and embodiments of the invention.

FIG. 5 is a flowchart illustrating an example of a computer-implemented method of the invention.

FIG. 6 is a diagram illustrating, conceptually, the combination of genetic and clinical factors into a combined residual risk score.

FIG. 7 is a diagram illustrating the process of determining (e.g., selecting SNPs for inclusion in) one optimal panel of SNPs (Panel 3) useful in various embodiments of this disclosure.

FIG. 8 is a histogram illustrating the cRRS remaining lifetime risk estimate range in Example 2 (from 0.88% to 66.4%), with 38.2% of patients having a lifetime risk >20% and 7.4% having a lifetime risk >35%. Such patients (i.e., with risk >20%) meet general guidelines, and are thus candidates, for increased screening for breast cancer (e.g., early and/or annual mammograms and/or breast MRI).

DETAILED DESCRIPTION OF THE DISCLOSURE Definitions

The following terms or definitions are provided solely to aid in the understanding of the disclosure. Additional definitions for other terms may be provided throughout this document. Further, terms given a general definition here in this section may be ascribed a more specific or different definition in another place of the disclosure that is applied to the indicated specific context. Unless specifically defined herein, all terms used herein have the same meaning as they would to one skilled in the art of the present disclosure. Practitioners are particularly directed to Sambrook et al., Molecular Cloning: A Laboratory Manual, 2^(nd) ed., Cold Spring Harbor Press, Plainsview, N.Y. (1989); and Ausubel et al., Current Protocols in Molecular Biology (Supplement 47), John Wiley & Sons, New York (1999), for definitions and terms of the art. Unless expressly defined otherwise herein, the terms used herein should not be construed to have a scope less than understood by a person of ordinary skill in the art.

As used herein, unless stated to the contrary, “about” means +/−10%, more preferably +/−5%, more preferably +/−1%, of the designated value.

As used herein, “algorithm” encompasses any formula, model, mathematical equation, algorithmic, analytical or programmed process, or statistical technique or classification analysis that takes one or more inputs or parameters, whether continuous or categorical, and calculates an output value, index, index value or score. Examples of algorithms include but are not limited to ratios, sums, regression operators such as exponents or coefficients, biomarker value transformations and normalizations (including, without limitation, normalization schemes that are based on clinical parameters such as age, gender, ethnicity, etc.), rules and guidelines, statistical classification models, and neural networks trained on populations. Also of use in the context of mutation load as described herein are linear and non-linear equations and statistical classification analyses to determine the relationship between (a) the number of mutations detected in a subject sample and (b) the level of the respective subject's mutation load.

As used herein, “allele” means one of two or more different nucleotide sequences (DNA or RNA) that occur or are encoded at a specific locus, or two or more different polypeptide sequences encoded by such a locus. For example, a first allele can occur on one chromosome, while a second allele occurs on a second homologous chromosome, e.g., as occurs for different chromosomes of a heterozygous individual, or between different homozygous or heterozygous individuals in a population. In the context of the genotype at a particular locus (e.g., a SNP locus), an allele generally refers to the nucleotide base present on chromosome (out of the expected two) at that specific locus. For example, at one particular SNP locus a patient may have an adenine (A) one chromosome and a guanine (G) one the other, in which case it can be said that the patient has one A allele and one G allele. As used herein, “homozygous” means an individual or subject has only one type of allele at a given locus (e.g., a diploid individual has a copy of the same allele at a locus for each of two homologous chromosomes, such as A/A in the preceding example). An individual is “heterozygous” if more than one allele type is present at a given locus (e.g., a diploid individual with one copy each of two different alleles, such as A/G in the preceding example). The term “homogeneity” indicates the degree to which members of a group have the same genotype at one or more specific loci. In contrast, the term “heterogeneity” is used to indicate the degree to which individuals within the group differ in genotype at one or more specific loci (e.g., all homozygous, all the same type of heterozygosity, etc.). An allele “positively” correlates with a trait when it is linked to it and when presence of the allele is an indicator that the trait or trait form will occur in an individual comprising the allele. An allele “negatively” correlates with a trait when it is linked to it and when presence of the allele is an indicator that a trait or trait form will not occur in an individual comprising the allele.

“Allele frequency” refers to the frequency (e.g., proportion or percentage) at which an allele (e.g., adenine versus guanine in the example above) is present at a locus within an individual, within a line or within a population (or subpopulation). In the above example, for an allele “A”, diploid individuals of genotype “A/A”, “A/G,” or “G/G” have allele frequencies of 1.0, 0.5, or 0.0, respectively. One can estimate the allele frequency within a line or population (e.g., cases or controls) by averaging the allele frequencies of a sampling of individuals from that line or population. Similarly, one can calculate the allele frequency within a population of lines by averaging the allele frequencies of linesthat make up the population. In some embodiments, the term “allele frequency” is used to define the minor allele frequency (MAF). MAF refers to the frequency at which the least common allele (where two alleles are observed) occurs in a given population, or the frequency at which the second most common allele (where more than two alleles are observed) occurs in a given population.

As used herein, “amplifying” in the context of nucleic acid amplification means any process or reaction whereby additional copies of a nucleic acid (or a transcribed form thereof) comprising a particular nucleotide sequence are produced. Amplification techniques include, but are not limited to, various polymerase based replication methods, including the polymerase chain reaction (PCR), ligase mediated methods such as the ligase chain reaction (LCR) and RNA polymerase based amplification (e.g., by transcription) methods. An “amplicon” is an amplified nucleic acid, e.g., a nucleic acid (or population of such nucleic acids, e.g., in solution) that is (or are) produced by amplifying a template nucleic acid by an amplification technique (e.g., PCR, LCR, transcription, or the like).

As used herein, the term “analyze” or “analyzing” generally includes “measure,” “measuring,” “detect,” “detecting,” “identify,” “identifying,” “assay,” “assaying,” “quantify,” or “quantifying,” and refers to the process of evaluating a biological sample (or a sample derived therefrom) for the presence, absence amount, level, or quality of some physical, chemical, or electromagnetic property(ies). This is often done by determining a value or set of values associated with such properties (e.g., number of sequencing reads in which a fluorescence signal indicating the presence of an adenine was observed at a particular position within the read corresponding to a particular position in a gene, chromosome or genome). Specific examples particularly relevant to the present disclosure include analyzing a sample to determining the sequence at one or more particular genomic loci in the sample, and may further comprise comparing test nucleotide sequence(s) detected in a patient's sample against reference nucleotide sequence(s) and/or comparing the test number of any such test sequences to one or more reference numbers of such reference sequences.

As used herein, “breast cancer” encompasses any type of breast cancer that can develop in a subject. For example, the breast cancer may be characterised as Luminal A (ER+ and/or PR+, HER2−, low Ki67), Luminal B (ER+ and/or PR+, HER2+(or HER2− with high Ki67), Triple negative/basal-like (ER−, PR−, HER2−) or HER2 type (ER−, PR−, HER2+). In another example, the breast cancer may be resistant to therapy or therapies such as alkylating agents, platinum agents, taxanes, vinca agents, anti-estrogen drugs, aromatase inhibitors, ovarian suppression agents, endocrine/hormonal agents, bisphophonate therapy agents or targeted biological therapy agents.

A locus (e.g., SNP) or allele is “correlated” or “associated” with a specified phenotype (e.g., increased risk of developing breast cancer) when it can be statistically linked (positively or negatively) to the phenotype. For example, a specified polymorphism may occur more commonly in a case population (e.g., breast cancer patients) than in a control population (e.g., individuals that do not have breast cancer). This correlation may suggest some natural or biological causal link (e.g., a natural law or phenomenon), but it typically does not prove or require such a link (i.e., the correlation is not such a law or phenomenon per se). As used herein, “correlation” refers instead to an artificial statistical linkage between a locus and a trait that underlies the phenotype.

As used herein, the term “diagnosis” refers to methods by which a determination can be made as to whether an individual has or is likely to have a given clinical characteristic (e.g., risk of developing cancer). The skilled artisan often makes a diagnosis on the basis of one or more diagnostic indicators, e.g., a biomarker, the presence, absence, amount, or change in amount of which may indicate the presence, severity, or absence of the condition. Other diagnostic indicators can include patient history; physical symptoms, e.g., unexplained weight loss, fever, fatigue, pains, or skin anomalies; phenotype; genotype; or environmental or heredity factors. A skilled artisan will understand that the term “diagnosis” often refers to an increased probability or likelihood that given clinical characteristic is present or will occur; that is, that a clinical characteristic is more likely to be present or to occur in a patient exhibiting a given feature, e.g., the presence or level of a diagnostic indicator, when compared to individuals not exhibiting the feature. Diagnostic methods can be used independently, or in combination with other diagnosing methods known in the art to determine whether a clinical characteristic is present or is more likely to occur in a patient exhibiting a given feature.

As used herein, “disease” can encompass any disorder, condition, sickness, ailment, etc. that manifests in, e.g., a disordered or incorrectly functioning organ, part, structure, or system of the body, and results from, e.g., genetic or developmental errors, infection, poisons, nutritional deficiency or imbalance, toxicity, or unfavorable environmental factors.

As used herein, “genotype” means the genetic constitution of an individual (or group of individuals) at one or more genetic loci. Genotype is defined by the allele(s) of one or more known loci of the individual, typically, the compilation of alleles inherited from its parents. In most aspects and embodiments of the present disclosure, the genotype will be the nucleotide (adenine, thymine (or uracil), cytosine, guanine) at a particular locus in either one or both (typically both) alleles of a subject's genome or chromosomes. With respect to a particular nucleotide position or locus, the nucleotide(s) at that locus or equivalent thereof in one or both alleles form the genotype of the locus. A genotype can typically be homozygous (e.g., A/A) or heterozygous (e.g., A/B), though more complex genotypes are possible (e.g., AA/A, AA/B, etc.). Accordingly, “genotyping” or determining the genotype for a particular locus means determining the nucleotide(s) at a particular gene locus. One example of this is “detecting” the genotype at a locus, which means determining through a a physical assay the physical presence (and optionally quantity) of the nucleotides at a given locus in a patient's genome or chromosomes. Genotyping can also be done by determining the amino acid variant at a particular position of a protein which can be used to deduce the corresponding nucleotide variant(s).

As used herein, “haplotype” means the genotype of an individual at a plurality of genetic loci on a single DNA strand. Typically, the genetic loci described by a haplotype are physically and genetically linked, i.e., on the same chromosome strand.

As used herein, “high stringency hybridization conditions,” when used in connection with nucleic acid hybridization, means conditions capable of restricting hybridization between nucleic acid molecules in a reaction to only those molecules sufficiently homologous to hybridize under the following conditions: hybridization conducted overnight at 42° C. in a solution containing 50% formamide, 5×SSC (750 mM NaCl, 75 mM sodium citrate), 50 mM sodium phosphate, pH 7.6, 5×Denhardt's solution, 10% dextran sulfate, and 20 microgram/ml denatured and sheared salmon sperm DNA, with hybridization filters washed in 0.1×SSC at about 65° C. In some embodiments “high stringency hybridization conditions” means the preceding hybridization conditions. The term “moderate stringent hybridization conditions,” when used in connection with nucleic acid hybridization, means conditions capable of restricting hybridization between nucleic acid molecules in a reaction to only those molecules sufficiently homologous to hybridize under the following conditions: hybridization conducted overnight at 37° C. in a solution containing 50% formamide, 5×SSC (750 mM NaCl, 75 mM sodium citrate), 50 mM sodium phosphate, pH 7.6, 5×Denhardt's solution, 10% dextran sulfate, and 20 microgram/ml denatured and sheared salmon sperm DNA, with hybridization filters washed in 1×SSC at about 50° C. In some embodiments “moderate stringency hybridization conditions” means the preceding hybridization conditions. It is noted that many other hybridization methods, solutions and temperatures can be used to achieve comparable stringent hybridization conditions as will be apparent to skilled artisans apprised of the present disclosure.

As used herein, a patient has an “increased risk” of a particular cancer if the probability of the patient developing that cancer (e.g., over the patient's lifetime, over some defined period of time (e.g., within 10 years), etc.) exceeds some reference probability or value. The reference probability may be the probability (i.e., prevalence) of the cancer across the general relevant patient population (e.g., all patients; all patients of a particular age, gender, ethnicity; patients having a particular cancer (and thus looking at the risk of a different cancer or an independent second primary of the same type as the first cancer); etc.). For example, if the lifetime probability of a particular cancer in the general population (or some specific subpopulation) is X % and a particular patient has been determined by the methods, systems or kits of the present disclosure to have a lifetime probability of that cancer of Y %, and if Y>X, then the patient has an “increased risk” of that cancer. Alternatively, the tested patient's probability may only be considered “increased” when it exceeds the reference probability by some threshold amount (e.g., at least 0.5, 0.75, 0.85, 0.90, 0.95, 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 or more fold or standard deviations greater than the reference probability; at least 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 60%, 70%, 80%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% greater than the reference probability).

The phrase “linkage disequilibrium” (LD) is used to describe the statistical correlation between two polymorphic genotypes (often neighboring). Non-limiting examples of loci in LD with test loci of the present disclosure are given in Table 1. Typically, LD refers to the correlation between the alleles of a random gamete at the two loci, assuming Hardy-Weinberg equilibrium (statistical independence) between gametes. LD can be quantified with either Lewontin's parameter of association (D′) or with Pearson correlation coefficient (r) (Devlin and Risch, 1995). Two loci with a LD value of 1 are generally said to be in complete LD. At the other extreme, two loci with a LD value of 0 are generallytermed to be in linkage equilibrium. Linkage disequilibrium can be calculated following the application of the expectation maximization algorithm (EM) for the estimation of haplotype frequencies (Slatkin and Excoffier, 1996). LD values according to the present disclosure for genotypes/loci are selected above 0.1, above 0.2, above 0.5, above 0.6, above 0.7, above 0.8, above 0.9, or about 1.0.

Another way one of skill in the art can identify SNPs in linkage disequilibrium with SNPs of the present disclosure is determining the LOD score for two loci. LOD stands for “logarithm of the odds”, a statistical estimate of whether two loci (e.g., or a locus and a disease locus) are likely to be located near each other on a chromosome and are therefore likely to be inherited together. A LOD score of between about 2-3 or higher is generally understood to suggest that two genes are located close to each other on the chromosome. In some embodiments, LOD values according to the present disclosure for genotypes/loci are selected at least above 2, at least above 3, at least above 4, at least above 5, at least above 6, at least above 7, at least above 8, at least above 9, at least above 10, at least above 20 at least above 30, at least above 40, at least above 50.

In some embodiments, SNPs in linkage disequilibrium with the SNPs of the present disclosure can have a specified genetic recombination distance of less than or equal to about 20 centimorgan (cM) or less. For example, 15 cM or less, 10 cM or less, 9 cM or less, 8 cM or less, 7 cM or less, 6 cM or less, 5 cM or less, 4 cM or less, 3 cM or less, 2 cM or less, 1 cM or less, 0.75 cM or less, 0.5 cM or less, 0.25 cM or less, or 0.1 cM or less. For example, two linked loci within a single chromosome segment can undergo recombination during meiosis with each other at a frequency of less than or equal to about 20%, about 19%, about 18%, about 17%, about 16%, about 15%, about 14%, about 13%, about 12%, about 11%, about 10%, about 9%, about 8%, about 7%, about 6%, about 5%, about 4%, about 3%, about 2%, about 1%, about 0.75%, about 0.5%, about 0.25%, or about 0.1% or less.

In some embodiments, SNPs in linkage disequilibrium with the SNPs of the present disclosure are within at least 100 kb (which correlates in humans to about 0.1 cM, depending on local recombination rate), at least 50 kb, at least 20 kb or less of each other.

One example approach for the identification of surrogate markers for a particular SNP involves a strategy that presumes that SNPs surrounding the target SNP are in linkage disequilibrium and can therefore provide information about disease susceptibility. Thus, as described herein, surrogate markers can therefore be identified from publicly available databases, such as HAPMAP, by searching for SNPs fulfilling certain criteria which have been found in the scientific community to be suitable for the selection of surrogate marker candidates.

As used herein, “locus” means a specific position or site in a gene (or protein), chromosomal region, chromosome, or genome. As used herein, a “test locus” is a genomic locus (e.g., single nucleotide at a specified position within a chromosome) whose sequence or genotype is assessed according to the present disclosure. A test locus in the present disclosure is often, though not necessarily, a single nucleotide polymorphism. As used herein, “single nucleotide polymorphism” or “SNP” means a genetic variation between individuals; e.g., a single nitrogenous base position in the DNA of organisms that is polymorphic or variable. As used herein, “SNPs” is the plural of SNP. The identifier used herein for SNP loci (e.g., in Tables 1 & 2) is the “rs” identifier often used in the art. This identifier is used, e.g., in the dbSNP database available through the NCBI website and may be updated for changed for any given locus over time. Thus, any “rs” identifier used herein is expressly meant to include new or modified “rs” identifiers assigned to the same locus (i.e., the locus to which the “rs” identifier is assigned in Tables 1 or 2 and in dbSNP as of the date of the filing of this disclosure. References to DNA herein may include derivatives of any given source of DNA such as amplicons, RNA transcripts thereof, etc. As used herein, a “polymorphism” or “polymorphic” locus or position is a locus or position that is variable; that is, within a population, the nucleotide sequence at a polymorphism has more than one version or allele. One example of a polymorphism is a “single nucleotide polymorphism.”

As used herein, “marker,” “molecular marker” or “marker nucleic acid” means to a nucleotide sequence or encoded product thereof (e.g., a protein) used as a point of reference when identifying a locus or a linked locus. A marker can be derived from genomic nucleotide sequence or from expressed nucleotide sequences (e.g., from an RNA, nRNA, mRNA, a cDNA, etc.), or from an encoded polypeptide. The term also refers to nucleic acid sequences complementary to or flanking the marker sequences, such as nucleic acids used as probes or primer pairs capable of amplifying the marker sequence. A “marker probe” is a nucleic acid sequence or molecule that can be used to identify the presence of a marker locus, e.g., a nucleic acid probe that is complementary to a marker locus sequence or to sequences adjacent to or near such marker locus sequence. Nucleic acids are “complementary” when they specifically hybridize in solution, e.g., according to Watson-Crick base pairing rules and at certain minimum hybridization conditions (e.g., medium stringency). A “marker locus” is a locus that can be used to track the presence of a second linked locus, e.g., a linked or correlated locus that encodes or contributes to the population variation of a phenotypic trait. For example, a marker locus can be used to monitor segregation of alleles at a locus that is genetically or physically linked to the marker locus. Thus, a “marker allele,” alternatively an “allele of a marker locus” is one of a plurality of polymorphic nucleotide sequences found at a marker locus in a population that is polymorphic for the marker locus. Each of the identified markers is expected to be in close physical and genetic proximity (resulting in physical and/or genetic linkage) to a genetic element that contributes to the relevant phenotype. Markers corresponding to genetic polymorphisms between members of a population can be analyzed (e.g., detected, measured, quantified) by several techniques. These include, e.g., PCR-based sequence specific amplification methods, detection of restriction fragment length polymorphisms (RFLP), detection of isozyme markers, detection of allele specific hybridization (ASH), detection of single nucleotide extension, detection of amplified variable sequences of the genome, detection of self-sustained sequence replication, detection of simple sequence repeats (SSRs), detection of single nucleotide polymorphisms (SNPs), or detection of amplified fragment length polymorphisms (AFLPs).

As used herein, “next generation sequencing” or “NGS” refers to a variety of high-throughput sequencing technologies that parallelize the sequencing process, producing thousands or millions of sequences at once. NGS is generally conducted with the following steps: First, DNA sequencing libraries are generated by clonal amplification by PCR in vitro; second, the DNA is sequenced by synthesis, such that the DNA sequence is determined by the addition of nucleotides to the complementary strand rather through chain-termination chemistry typical of Sanger sequencing; third, the spatially segregated, amplified DNA templates are sequenced simultaneously in a massively parallel fashion, typically without the requirement for a physical separation step. NGS parallelization of sequencing reactions can generate hundreds of megabases to gigabases of nucleotide sequence reads in a single instrument run. Unlike conventional sequencing techniques, such as Sanger sequencing, which typically report the average genotype of an aggregate collection of molecules, NGS technologies typically digitally tabulate the sequence of numerous individual DNA fragments (sequence reads discussed in detail below), such that low frequency variants (e.g., variants present at less than about 10%, 5% or 1% frequency in a heterogeneous population of nucleic acid molecules) can be detected. The term “massively parallel” can also be used to refer to the simultaneous generation of sequence information from many different template molecules by NGS.

NGS strategies can include several methodologies, including, but not limited to: (i) microelectrophoretic methods; (ii) sequencing by hybridization; (iii) real-time observation of single molecules, and (iv) cyclic-array sequencing. Cyclic-array sequencing refers to technologies in which a sequence of a dense array of DNA is obtained by iterative cycles of template extension and imaging-based data collection. Commercially available cyclic-array sequencing technologies include, but are not limited to 454 sequencing, for example, used in 454 Genome Sequencers (Roche Applied Science; Basel), Solexa technology, for example, used in the Illumina Genome Analyzer, Illumina HiSeq, MiSeq, and NextSeq (San Diego, Calif.), the SOLiD platform (Applied Biosystems; Foster City, Calif.), the Polonator (Dover/Harvard) and HeliScope Single Molecule Sequencer technology (Helicos; Cambridge, Mass.). Other NGS methods include single molecule real time sequencing (e.g., Pacific Bio) and ion semiconductor sequencing (e.g., Ion Torrent sequencing). See, e.g., Shendure & Ji, Next Generation DNA Sequencing, NAT. BIOTECH. (2008) 26:1135-1145 for a more detailed discussion of NGS sequencing technologies.

As used herein, “patient” or “individual” or “subject” refers to a human. A subject can be male or female.

As used herein, “sample” or “biological sample” refers to samples such as biopsy or tissue samples, frozen samples, blood and blood fractions or products (e.g., serum, platelets, red blood cells, and the like), tumor samples, sputum, bronchoalveolar lavage, cultured cells, e.g., primary cultures, explants, and transformed cells, stool, urine, etc. A “biopsy” refers to the process of removing a tissue sample for diagnostic or prognostic evaluation, and to the tissue specimen derived from such a process. Any suitable biopsy technique can be applied to the methods of the present disclosure. The biopsy technique applied will depend on the tissue type to be evaluated (e.g., lung etc.), the size and type of the tumor, among other factors. Representative biopsy techniques include, but are not limited to, excisional biopsy, incisional biopsy, needle biopsy, surgical biopsy, and bone marrow biopsy. An “excisional biopsy” refers to the removal of an entire tumor mass with a small margin of normal tissue surrounding it. An “incisional biopsy” refers to the removal of a wedge of tissue that includes a cross-sectional diameter of the tumor. A diagnosis made by endoscopy or fluoroscopy can require a “core-needle biopsy”, or a “fine-needle aspiration biopsy” which generally obtains a suspension of cells from within a target tissue. A “bodily fluid” include all fluids obtained from a mammalian body, either processed (e.g., serum) or unprocessed, which can include, for example, blood, plasma, urine, lymph, gastric juices, bile, serum, saliva, sweat, and spinal and brain fluids. A biological sample is typically obtained from a subject. As used herein, “cancer cell samples” or “tumor sample” means a specimen comprising either at least one cancer cell or biomolecules derived therefrom, including without limitation, lung cancer (e.g., non-small cell lung cancer (NSCLC)), ovarian cancer, colorectal cancer, breast cancer, endometrial cancer, or prostate cancer. Non-limiting examples of such biomolecules include nucleic acids and proteins. Biomolecules “derived” from a cancer cell sample include molecules located within or extracted from the sample as well as artificially synthesized copies or versions of such biomolecules. One illustrative, non-limiting example of such artificially synthesized molecules includes PCR amplification products in which nucleic acids from the sample serve as PCR templates. “Nucleic acids of” a cancer cell sample include nucleic acids located in a cancer cell or biomolecules derived from a cancer cell.

As used herein, “sequence read” means the sequence of an individual DNA molecule sequenced in a sequencing reaction. Especially in next-generation sequencing, individual DNA molecules used for sequencing can be relatively short (e.g., ranging from 50 nt to 1,000 nt). These molecules are typically heavily overlapping in their sequences. Thus, any individual test locus is contained within numerous distinct DNA molecules in the sample. When each individual molecule is sequenced (often in parallel), the numerous resulting “sequence reads” can be aligned against each other and/or against a larger reference sequence (e.g., a reference human genome sequence such as the hg19 version of the human genome assembly available at the University of California Santa Clara's Genome Browser website). Generally speaking, a greater number of reliably sequenced (or “informative”) reads containing (or “covering”) any individual locus yields greater accuracy and confidence in the genotype/sequence at that locus. Thus, in some specific embodiments of each of the above aspects of the disclosure a test locus (or an allele at that locus) may be counted only if it is covered by at least some minimal number of sequence reads in the sequencing reaction(s).

As used herein, “score” means a value or set of values selected so as to provide a quantitative measure or assessment of a variable or characteristic of a subject or the subject's condition or physiology. The value(s) comprising the score can be based on, derived from or incorporate, for example, quantitative data resulting in a measured amount of one or more sample constituents obtained from the subject. In certain embodiments the score can be derived from a single constituent, parameter or assessment, while in other embodiments the score is derived from multiple constituents, parameters and/or assessments. The score can be based upon or derived from an interpretation function; e.g., an interpretation function derived from a particular predictive model using any of various statistical algorithms. A “change in score” can refer to the absolute change in score, e.g. from one time point to the next, or the percent change in score, or the change in the score per unit time (i.e., the rate of score change).

As used herein, the term “treatment” or “therapy” or “therapeutic regimen” includes all clinical management of a subject and interventions, whether biological, chemical, physical, or a combination thereof, intended to sustain, ameliorate, improve, or otherwise alter the condition of a subject. These terms may be used synonymously herein. Treatments include but are not limited to administration of prophylactics or therapeutic compounds (including small molecule and biologic drugs), exercise regimens, physical therapy, dietary modification and/or supplementation, bariatric surgical intervention, administration of therapeutic compounds (prescription or over-the-counter), and any other treatments known in the art as efficacious in preventing, delaying the onset of, or ameliorating disease characterized by HML. A “response to treatment” includes a subject's response to any of the above-described treatments, whether biological, chemical, physical, or a combination of the foregoing. A “treatment course” relates to the dosage, duration, extent, etc. of a particular treatment or therapeutic regimen. An initial therapeutic regimen as used herein is the first line of treatment.

As used herein, “variant allele ratio” means the proportion of informative sequence reads harboring a particular nucleotide at a specific locus as a proportion of the total sequence reads. For example, if a test locus is covered by 100 informative sequence reads in a particular sequencing reaction and 15 reads carry a particular nucleotide (e.g., a risk modifying allele), then the risk modifying allele ratio is 15%. In some contexts variant allele ratios that are too low or too high may indicate unreliability in an allele or genotype call (sometimes referred to herein as a call failure). For example, if the variant allele ratio is around 1%, this can in many cases be due to sequencing artifacts and noise (e.g., a small proportion of sequence reads simply contain sequencing errors). Thus, in some specific embodiments of each of the above aspects of the disclosure a test locus (or an allele or specific nucleotide at that locus) may be counted only if the variant allele ratio is within a specific (e.g., pre-specified) range.

Aspects of the Disclosure

The present disclosure generally relates to methods, systems and kits for detecting genotypes at polymorphic loci in a biological specimen and methods, systems and kits for clinically applying such genotype detection in detecting risk for developing cancer and, in turn, directing clinical management. Some disclosed methods generally involve analyzing a plurality of genomic loci to detect a subject's genotype at these loci and estimating the subject's risk for developing cancer based at least in part on the detection of these genotypes. The present disclosure reports data enabling methods, systems and kits utilizing the loci (and panels of loci) listed in Table 1.

TABLE 1 (Panel 1) SNP SNP SNP SNP Designation Designation Designation Designation Panel 1 chr17: rs12662670 rs2236007 rs6507583 29230520:D*‡ rs10069690 rs12710696* rs2363956* rs6678914*‡ rs1011970 rs1292011 rs2380205*‡ rs6762644 rs1045485*‡ rs13162653 rs2588809 rs6796502 rs10472076 rs132390 rs2736108 rs6828523 rs1053338 rs13267382 rs2823093 rs6964587 rs10759243 rs13281615 rs2943559 rs704010 rs10771399 rs13329835 rs2981579 rs7072776 rs10941679 rs13365225 rs3760982 rs720475 rs10995190 rs13387042 rs3803662 rs72755295 rs11075995*‡ rs1353747 rs3817198 rs745570 rs11199914 rs1432679 rs3903072 rs75915166 rs11242675 rs1436904 rs4245739*‡ rs7707921 rs11249433 rs1550623 rs4593472 rs7726159 rs11552449 rs16857609 rs4808801 rs78540526 rs11571833 rs17356907 rs4849887 rs7904519 rs11621587 rs17529111 rs4973768 rs8170* (LD SNP for rs11627032)*‡ rs11627032 rs17817449 rs527616 rs865686 rs11780156 rs17879961 rs554219 rs889312 rs11814448 rs186951 rs6001930 rs9257408*‡ (LD SNP for rs13162653)*‡ rs11820646 rs2012709 rs616488 rs941764 rs12048493 rs2016394 rs62070644 rs9693444 (LD SNP for chr17:29230520:D)* rs12405132 rs204247 rs6472903 rs9790517* rs12422552*‡ rs2046210 rs6504950 rs999737 rs12493607 Panel 2 rs10069690 rs12710696* rs2363956* rs6507583 rs1011970 rs1292011 rs2380205*‡ rs6678914*‡ rs1045485*‡ rs13162653 rs2588809 rs6762644 rs10472076 rs132390 rs2736108 rs6796502 rs1053338 rs13267382 rs2823093 rs6828523 rs10759243 rs13281615 rs2943559 rs6964587 rs10771399 rs13329835 rs2981579 rs704010 rs10941679 rs13365225 rs3760982 rs7072776 rs10995190 rs13387042 rs3803662 rs720475 rs11075995*‡ rs1353747 rs3817198 rs72755295 rs11199914 rs1432679 rs3903072 rs745570 rs11242675 rs1436904 rs4245739*‡ rs75915166 rs11249433 rs1550623 rs4593472 rs7707921 rs11552449 rs16857609 rs4808801 rs7726159 rs11571833 rs17356907 rs4849887 rs78540526 rs11627032 rs17529111 rs4973768 rs7904519 rs11780156 rs17817449 rs527616 rs8170* rs11814448 rs17879961 rs554219 rs865686 rs11820646 rs2012709 rs6001930 rs889312 rs12405132 rs2016394 rs616488 rs941764 rs12422552*‡ rs204247 rs62070644 rs9693444 (LD SNP for chr17:29230520:D)* rs12493607 rs2046210 rs6472903 rs9790517* rs12662670 rs2236007 rs6504950 rs999737 Panel 3 rs10069690 rs13162653 rs2588809 rs6796502 rs1011970 rs132390 rs2736108 rs6828523 rs10472076 rs13267382 rs2823093 rs6964587 rs1053338 rs13281615 rs2943559 rs704010 rs10759243 rs13329835 rs2981579 rs7072776 rs10771399 rs13365225 rs3760982 rs720475 rs10941679 rs13387042 rs3803662 rs72755295 rs10995190 rs1353747 rs3817198 rs745570 rs11199914 rs1432679 rs3903072 rs75915166 rs11242675 rs1436904 rs4593472 rs7707921 rs11249433 rs1550623 rs4808801 rs7726159 rs11552449 rs16857609 rs4849887 rs78540526 rs11571833 rs17356907 rs4973768 rs7904519 rs11627032 rs17529111 rs527616 rs8170* rs11780156 rs17817449 rs554219 rs865686 rs11814448 rs17879961 rs6001930 rs889312 rs11820646 rs2012709 rs616488 rs941764 rs12405132 rs2016394 rs62070644 rs9693444 (LD SNP for chr17:29230520:D)* rs12493607 rs204247 rs6472903 rs9790517* rs12662670 rs2046210 rs6504950 rs999737 rs12710696* rs2236007 rs6507583 rs1292011 rs2363956* rs6762644 Panel 4 rs10069690 rs1292011 rs2236007 rs6762644 rs1011970 rs13162653 rs2588809 rs6796502 rs10472076 rs132390 rs2736108 rs6828523 rs1053338 rs13267382 rs2823093 rs6964587 rs10759243 rs13281615 rs2943559 rs704010 rs10771399 rs13329835 rs2981579 rs7072776 rs10941679 rs13365225 rs3760982 rs720475 rs10995190 rs13387042 rs3803662 rs72755295 rs11199914 rs1353747 rs3817198 rs745570 rs11242675 rs1432679 rs3903072 rs75915166 rs11249433 rs1436904 rs4593472 rs7707921 rs11552449 rs1550623 rs4808801 rs7726159 rs11571833 rs16857609 rs4849887 rs78540526 rs11627032 rs17356907 rs4973768 rs7904519 rs11780156 rs17529111 rs527616 rs865686 rs11814448 rs17817449 rs554219 rs889312 rs11820646 rs17879961 rs6001930 rs941764 rs12048493 rs2012709 rs616488 rs9693444 rs12405132 rs2016394 rs6472903 rs999737 rs12493607 rs204247 rs6504950 rs12662670 rs2046210 rs6507583

Accordingly, one aspect of the present disclosure relates to a method for genotyping a subject comprising: analyzing a sample of DNA obtained or derived from the subject to detect the genotype for a plurality of test genomic loci, wherein the plurality consists of at most X loci and comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, or more loci or a panel of loci listed in Table 1, and/or for one or more of such test genomic locus a locus in linkage disequilibrium with one or more thereof.

Another aspect of the present disclosure relates to a method for assessing risk for developing cancer comprising:

-   -   (1) analyzing a sample of DNA obtained or derived from a subject         to detect the subject's genotype for a plurality of genomic loci         (e.g., at most X genomic loci) comprising a plurality of test         loci comprising at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,         14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65,         70, 75, 80, or more of the loci listed in Table 1, and/or for         one or more of such test locus a locus in linkage disequilibrium         with one or more thereof; and     -   (2) determining (or estimating) the subject's risk for         developing cancer based at least in part on the genotypes         detected in (1).

In another aspect of the present disclosure, a combined determination (or estimate) of risk is made by combining clinical characteristics of the subject with the subject's genotypes for the test genomic loci. Accordingly, another aspect of the present disclosure relates to a method for assessing risk for developing cancer comprising:

-   -   (1) analyzing a sample of DNA obtained or derived from a subject         to detect the subject's genotype for a plurality of genomic loci         (e.g., at most X genomic loci) comprising a plurality of test         loci comprising at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,         14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65,         70, 75, 80, or more of the loci listed in Table 1, and/or for         one or more of such test genomic locus a locus in linkage         disequilibrium with one or more thereof;     -   (2) evaluating one or more clinical characteristics of the         subject; and     -   (3) determining (or estimating) the subject's risk for         developing cancer based at least in part on the genotypes         detected in (1) and the clinical characteristic(s) evaluated in         (2).

In another aspect of the present disclosure, the subject's genotypes for the test loci are incorporated into a test score (e.g., numerical value), which can in turn be used to diagnose a risk of cancer that is above (or not above) a reference risk level. Accordingly, another aspect of the present disclosure relates to a method for assessing risk for developing cancer comprising:

-   -   (1) analyzing a sample of DNA obtained or derived from a subject         to detect the subject's genotype for a plurality of genomic loci         (e.g., at most X genomic loci) comprising a plurality of test         loci comprising at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,         14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65,         70, 75, 80, or more of the loci listed in Table 1, and/or for         one or more of such test genomic locus a locus in linkage         disequilibrium with one or more thereof;     -   (2) calculating a test score incorporating the genotypes         detected in (1); and     -   (3)(a) diagnosing a subject for whom the score in (2) exceeds a         reference score as having a test likelihood of developing cancer         that is higher than a reference likelihood of developing cancer;         or     -   (3)(b) diagnosing a subject for whom the score in (2) does not         exceed a reference score as having a test likelihood of         developing cancer that is equal to or lower than a reference         likelihood of developing cancer.

In another aspect of the present disclosure, the test score is a combined score incorporating both the genotypes detected in the subject and the subject's clinical characteristics. Accordingly, another aspect of the present disclosure relates to a method for assessing risk for developing cancer comprising:

-   -   (1) analyzing a sample of DNA obtained or derived from a subject         to detect the subject's genotype for a plurality of genomic loci         (e.g., at most X genomic loci) comprising a plurality of test         loci comprising at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,         14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65,         70, 75, 80, or more of the loci listed in Table 1, and/or for         one or more of such test genomic locus a locus in linkage         disequilibrium with one or more thereof;     -   (2) evaluating one or more clinical characteristics of the         subject;     -   (3) calculating a test score incorporating the genotypes         detected in (1) and the clinical characteristic(s) evaluated in         (2); and     -   (4)(a) diagnosing a subject for whom the score in (2) exceeds a         reference score as having a test likelihood of developing cancer         that is higher than a reference likelihood of developing cancer;         or     -   (4)(b) diagnosing a subject for whom the score in (2) does not         exceed a reference score as having a test likelihood of         developing cancer that is equal to or lower than a reference         likelihood of developing cancer.

Another aspect of the present disclosure relates to a method of screening for cancer in a subject comprising assessing the risk of the subject for developing cancer as disclosed in any aspect of the present disclosure, and screening for cancer (or recommending or prescribing screening for cancer) in a subject determined to have a risk for developing breast cancer that is increased (e.g., over general population risk) or is above some threshold (e.g., greater than 15%, 20%, 25%, 30%, 35%, 40%, 50%, 60%, 70%, 80%, 90% or greater risk of developing cancer).

Another aspect of the present disclosure relates to methods of treating subjects. This may include a method for treating a subject comprising:

-   -   (1) determining the subject's genotype for a plurality of         genomic loci (e.g., at most X genomic loci) comprising a         plurality of test loci comprising at least 2, 3, 4, 5, 6, 7, 8,         9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40,         45, 50, 55, 60, 65, 70, 75, 80, or more of the loci listed in         Table 1, and/or for one or more of such test genomic locus a         locus in linkage disequilibrium with one or more thereof;     -   (2) determining (or estimating) the subject's risk for         developing cancer based at least in part on the genotypes         determined in (1);     -   (3) prescribing or administering one or more treatment         modalities indicated based at least in part on the risk         determined (or estimated) in (2).

This may also include a method for preventing cancer in a subject comprising assessing the risk of the subject for developing cancer as disclosed in any aspect of the present disclosure, and administering an anti-cancer therapy (e.g., medical management that includes some action meant to prevent cancer) to a subject determined to have a risk for developing breast cancer that is increased (e.g., over general population risk) or is above some threshold (e.g., greater than 15%, 20%, 25%, 30%, 35%, 40%, 50%, 60%, 70%, 80%, 90% or greater risk of developing cancer).

This may also include an anti-breast cancer therapy for use in preventing breast cancer in a subject at risk thereof, wherein the risk of the subject for developing cancer is determined as disclosed in any aspect of the present disclosure. In some embodiments, the therapy inhibits estrogen.

Another aspect of the disclosure provides a kit for genotyping a subject comprising: a compartmentalized container; reagents for analyzing a sample of DNA obtained or derived from the subject to detect the genotype for a plurality of test genomic loci, wherein the plurality consists of at most Xgenomic loci and comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, or more test loci listed in Table 1, and/or for one or more of such test genomic locus a locus in linkage disequilibrium with one or more thereof; and instructions for use of the reagents.

Another aspect of the disclosure provides a system for genotyping a subject, comprising: (1) a sample analyzer for analyzing DNA in, extracted from or derived from a sample of the subject to detect the subject's genotype for a plurality of genomic loci (e.g., at most X genomic loci) comprising a plurality of test loci comprising at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, or more of the loci listed in Table 1, and/or for one or more of such test genomic locus a locus in linkage disequilibrium with one or more thereof, wherein the sample analyzer contains (a) the sample, (b) genomic DNA from the sample, (c) transcript RNA from the sample, or (d) DNA derived (e.g., synthesized or amplified) from said genomic DNA; (2) one or more computer programs for performing any one, all, or any combination of the following functions (a) a computer program for querying the subject's genotype data; (b) calculating the subject's risk for developing cancer based at least on the subject's genotype data; (c) calculating a test score based on or derived from the subject's genotype data; (d) comparing the test score to one or more reference scores; (e) concluding whether there is an increased risk of cancer based at least in part on the comparison in (d); and (f) outputting (e.g., displaying) the conclusion in (e).

Another aspect of the present disclosure provides computer program products comprising a computer-usable medium having computer-readable program codes or instructions embodied thereon for enabling a processor to carry out the methods of the disclosure. Generally speaking, the computer-usable medium comprises (1) a computer program for receiving, storing, and/or retrieving a subject's genotype data for a plurality of genomic loci (e.g., at most X genomic loci) comprising a plurality of test loci comprising at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, or more of the loci listed in Table 1, and/or for one or more of such test genomic locus a locus in linkage disequilibrium with one or more thereof; and (2) one or more computer programs for performing any one, all, or any combination of the following functions (a) a computer program for querying the subject's genotype data; (b) calculating the subject's risk for developing cancer based at least on the subject's genotype data; (c) calculating a test score based on or derived from the subject's genotype data; (d) comparing the test score to one or more reference scores; (e) concluding whether there is an increased risk of cancer based at least in part on the comparison in (d); and (f) outputting (e.g., displaying) the conclusion in (e). In some embodiments this program for outputting the conclusion may comprise a computer program for informing a health care professional of the conclusion.

Another aspect of the present disclosure relates to a computer implemented method for assessing the risk of a subject for developing cancer, the method operable in a computing system comprising a processor and a memory, the method comprising:

-   -   (1) receiving genetic risk data for the subject, wherein the         genetic risk data was obtained by detecting, in a sample derived         from the subject, the genotype for a plurality of genomic loci         (e.g., at most X genomic loci) comprising a plurality of test         loci comprising at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,         14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65,         70, 75, 80, or more of the loci listed in Table 1, and/or for         one or more of such test genomic locus a locus in linkage         disequilibrium with one or more thereof;     -   (2) optionally processing the data to combine the genetic risk         data with clinical risk data to obtain the risk of the subject         for developing cancer;     -   (3) outputting the risk of the subject for developing cancer.

In some embodiments, the clinical risk data and/or genetic risk data for the subject are received from a user interface coupled to the computing system. In some embodiments, the clinical risk data and/or genetic risk data for the subject are received from a remote device across a wireless communications network. In some embodiments, outputting comprises outputting information to a user interface coupled to the computing system. In some embodiments, outputting comprises transmitting information to a remote device across a wireless communications network.

Another aspect of the present disclosure relates to a system configured to perform the disclosed methods. This may include a system for assessing the risk of a subject for developing cancer comprising:

-   -   (a) system instructions for performing a genetic risk assessment         of the subject according to any of the method aspects of the         present disclosure; and     -   (b) optionally system instructions for performing a clinical         risk assessment of the subject;     -   (c) optionally system instructions for combining the clinical         risk assessment with the genetic risk assessment to obtain the         risk of a human female subject for developing breast cancer.

As will be apparent, at least some features of the methods, kits and systems can be used together in combination. For example, systems for identifying correlations between breast cancer susceptibility and polymorphisms can be used for practicing the methods herein. Kits can be used for practicing the methods herein. Thus, described features of the systems, methods and kits can be applied to the different systems, methods and kits herein.

FURTHER EMBODIMENTS OF THESE ASPECTS

The following section describes numerous specific embodiments of the more general aspects of the disclosure described in the preceding section. Unless expressly stated otherwise (i.e., unless an embodiment is expressly stated to apply to one ore more aspects and not to others), each description of each of these embodiments hereby incorporates by reference the details of each aspect described in the preceding section as if fully reproduced. As a non-limiting and illustrative example, the following paragraph describes specific embodiments in which the number of genomic loci analyzed is at most some specific number, without reproducing the other elements of any method described above (e.g., the step of analyzing a sample of DNA obtained or derived from the subject to detect the genotype for a plurality of test genomic loci, wherein the plurality consists of at most X loci and comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, or more loci or a panel of loci listed in Table 1, and/or for one or more of such test genomic locus a locus in linkage disequilibrium with one or more thereof). This is because such reproduction is not necessary to describe this embodiment since every such description of every specific embodiment, by virtue of this paragraph, already incorporates by reference all such additional elements of the method described in the preceding section. In other words, each description of a specific embodiment in the present section typically expands on and specifies one element of a more general aspect of the disclosure by substituting its more general counterpart (e.g., simply “at most X”) in the preceding section with expanded specific versions in the present section (e.g., X is at most 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1,000, [ . . . ]). Another non-limiting, illustrative example involves specific embodiments of the step of “analyzing a sample of DNA obtained or derived from a subject to detect the subject's genotype for a plurality of genomic loci (e.g., at most Xgenomic loci) comprising a plurality of test loci comprising at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, or more of the loci listed in Table 1, and/or for one or more of such test genomic locus a locus in linkage disequilibrium with one or more thereof”. Any specific embodiments described in the present section (e.g., where “analyzing” comprises sequencing genomic DNA to detect genotypes, where “analyzing” comprises microarray analysis to detect genotypes, etc.) apply to and can be interchanged with each instance of this step described in every aspect of the disclosure in the preceding section that includes this step.

Accordingly, in some embodiments of each of the above aspects of the disclosure, X (the number of genomic loci analyzed) is at most 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1,000, 1,250, 1,500, 1,750, 2,000, 2,500, 3,000, 3,500, 4,000, 4,500, 5,000, 5,500, 6,000, 6,500, 7,000, 7,500, 8,000, 8,500, 9,000, 9,500, 10,000, 11,000, 12,000, 13,000, 14,000, 15,000, 16,000, 17,000, 18,000, 19,000, 20,000, 25,000, 30,000, 35,000, 40,000, 45,000, 50,000, 55,000, 60,000, 65,000, 70,000, 75,000, 80,000, 85,000, 90,000, 95,000, 100,000, 125,000, 150,000, 175,000, 200,000, 225,000, 250,000, 275,000, 300,000, 325,000, 350,000, 375,000, 400,000, 425,000, 450,000, 475,000, 500,000, 550,000, 600,000, 650,000, 700,000, 750,000, 800,000, 850,000, 900,000, 950,000, 1,000,000, 1,250,000, 1,500,000, 1,750,000, 2,000,000, 2,500,000, 3,000,000, 3,500,000, 4,000,000, 4,500,000, or 5,000,000 loci.

In some embodiments determining (or estimating) the subject's risk for developing cancer based at least in part on the genotypes detected in (1) comprises calculating the subject's percent probability of developing cancer based at least in part on the genotypes detected in (1). In some embodiments such calculating comprises calculating a test score incorporating the genotypes detected in (1) and calculating the subject's percent probability of developing cancer based at least in part on such test score. In some such embodiments calculating the subject's percent probability of developing cancer based at least in part on the test score comprises multiplying the test score by some other value. In some such embodiments such other value is another score (optionally termed a clinical score) that is (or corresponds to) the subject's percent probability of developing cancer attributable to any single, any set or all known factors other than the genotypes detected in (1). In some embodiments the subject's percent probability of developing cancer based at least in part on the test score is calculated according to the following formula:

Test Score=1−(1−[Clinical Score])^((exp(C1*(Test Score)+C2)))  Formula (I):

where C1 and C2 are constants. In some embodiments the clinical score is the subject's Tyrer-Cuzick score or percent probability of developing cancer.

In some embodiments the test score is calculated by one or more or all of the following: assigning a coefficient or weight to each test locus, assigning a genotype value to the presence or absence of one or more possible alleles at each locus, multiplying each genotype value by a coefficient to derive a weighted genotype value for each locus, summing the weighted genotype values to derive a raw test score, and scaling the raw test score to derive a final test score. In some embodiments the test score is calculated by assigning a coefficient to each test locus (e.g., all or a panel of the loci in Table 1), assigning a genotype value corresponding to number of risk modifying alleles detected at each locus, multiplying each genotype value by the coefficient to derive a weighted genotype value for each locus, summing the weighted genotype values to derive the test score. In some embodiments the weighted genotype value is derived by further centering each genotype value by a mean allele copy value.

In some embodiments determining (or estimating or calculating) the subject's risk for developing cancer comprises determining (or estimating or calculating) the subject's risk for developing cancer attributable or allocable to the detected or determined genotypes. Embodiments where determining (or estimating) the subject's risk for developing cancer comprises combining the subject's risk for developing cancer attributable or allocable to the detected genotypes with a clinical risk assessment or other risk factors, calculations, scores, or estimations to yield a composite risk score or estimation. Thus, some embodiments further comprise performing a clinical risk assessment. In some such embodiments the clinical risk assessment comprises obtaining information from the subject on one or more of the following: medical history of breast cancer, ductal carcinoma or lobular carcinoma, age, age of first menstrual period, age at which the subject first gave birth, family history of breast cancer, results of previous breast biopsies, breast density, and race/ethnicity. In related embodiments, calculating a test score incorporating the genotypes detected in (1) and the clinical characteristic(s) evaluated in (2) comprises calculating a clinical score as described above. In some embodiments the clinical score is based at least in part on phenotypic risk factors including age, family history, reproductive history, and benign breast disease. In some embodiments the clinical score is calculated using: a Gail Model, a Claus Model, Claus Tables, BOADICEA, a Jonker Model, a Claus Extended Formula, a Tyrer-Cuzick Model, BRCAPRO, or a Manchester Scoring System. In some such embodiments the clinical score is a score or risk estimation calculated using a Tyrer-Cuzick Model (e.g., Tyrer-Cuzick version 7 or version 8).

In some embodiments such determining comprises determining whether the subject's probability of developing cancer exceeds some reference probability. In some such embodiments, such determining comprises comparing the test score to a reference score or probability. In some embodiments determining that the subject's probability of developing cancer exceeds some reference probability comprises calculating a test score incorporating the genotypes detected in (1) and calculating (or estimating) the subject's percent probability of developing cancer based at least in part on such test score exceeding some reference score. In some such embodiments such reference score corresponds to at least a 15%, 20%, 25%, 30%, 35%, 40%, 50%, 60%, 70%, 80%, 90% or greater probability of developing cancer.

In some embodiments of each aspect of the present disclosure the subject lacks or has been determined to not harbor a pathogenic variant in one or more genes. Some embodiments of the present disclosure further comprise (preferably before or concurrently with step (1) of the above-described methods) determining whether the patient harbors a pathogenic variant in one or more genes. In some embodiments one or more specimens is analyzed for both variants in such genes and to detect the genotype for the test loci and the determination of risk attributable or allocable to such genotypes is performed only if the patient is determined to not harbor any variants in such genes. In some embodiments determining whether the patient harbors a pathogenic variant in such genes comprises sequencing such genes (e.g., sequencing the coding portions of such genes). In some embodiments such sequencing is performed on the same sample as the genotype analysis while in other embodiments the sequencing is performed on another sample. In some embodiments such sequencing is performed using a next-generation sequencing technique.

In some embodiments such genes are one, a panel of, or all known cancer predisposition genes. In some embodiments such genes are one, a panel of, or all known moderate or high penetrance cancer predisposition genes. In some embodiments such genes are one, a panel of, or all known high penetrance cancer predisposition genes. In some embodiments a moderate penetrance cancer predisposition gene is a gene in which pathogenic variants have been shown in a peer-reviewed publication to increase a subject's risk of developing at least one cancer at least 2- or 3-fold over the average risk in a reference population. In some embodiments a high penetrance cancer predisposition gene is a gene in which pathogenic variants have been shown in a peer-reviewed publication to confer a risk of at least 25%, 30%, 35%, 40%, 45%, 50%, 60%, 70%, 80%, or 90% of developing at least one cancer. In some embodiments of each aspect of the present disclosure the subject lacks or has been determined to not harbor a pathogenic variant in one or more genes or gene panels in Table 2 (i.e., any genes or panels in any of Tables 2A, 2B, 2C, or 2D).

TABLE 2A Gene # Panel B Panel C Panel D Panel E Panel F Panel G 1 BRCA1 BRCA1 BRCA1 MLH1 BRCA1 BRCA1 2 BRCA2 BRCA2 BRCA2 MSH2 BRCA2 BRCA2 3 MLH1 MLH1 CHEK2 MSH6 MLH1 MLH1 4 MSH2 MSH2 ATM PMS2 MSH2 MSH2 5 MSH6 MSH6 NBN BRCA1 MSH6 MSH6 6 PMS2 PMS2 PALB2 BRCA2 PMS2 PMS2 7 EPCAM EPCAM BARD1 ATM EPCAM EPCAM 8 MUTYH MUTYH BRIP1 BARD1 APC APC 9 APC APC PMS2 BRIP1 MUTYH MUTYH 10 CDKN2A CDKN2A MSH2 CHEK2 PALB2 PALB2 11 PALB2 PALB2 MSH6 MUTYH CDKN2A CHEK2 12 SMAD4 SMAD4 TP53 RAD50 CDK4 PTEN 13 BMPR1A BMPR1A MUTYH EPCAM* TP53 STK11 14 TP53 TP53 PTEN CDH1 15 PTEN PTEN CDH1 TP53 16 STK11 STK11 STK11 ATM 17 CDH1 CDH1 SMAD4 RAD51C 18 NBN1 NBN1 BMPR1A RAD51D 19 CHEK2 CHEK2 ATM BRIP1 20 RAD51C RAD51C CHEK2 BARD1 21 RAD51D RAD51D RAD51C BMPR1A 22 BRIP1 BRIP1 RAD51D SMAD4 23 BARD1 BARD1 MLH3 CDKN2A 24 ATM ATM BRIP1 CDK4 25 CDK4 CDK4 BARD1 RAD50 26 RAD50* NSB1 NBN 27 MRE11A* RAD50 MRE11 28 MLH3* MRE11A MLH3 29 MITF* HOXB13* 30 ELAC2* *Optional in this panel

TABLE 2B Panels H to M Gene # Panel H Panel I Panel J Panel K Panel L Panel M Panel N 1 APC ATM APC BLM ATR BRCA1 BRCA1 2 BRCA1 BMPR1A ATM CEBPA BARD1 BRCA2 BRCA2 3 BRCA2 CDH1 BMPR1A FLCN BRAF MLH1 MLH1 4 CDKN2A CDK4 BRCA1 MEN1 BRIP1 MSH2 MSH2 5 EPCAM CHEK2 BRCA2 PTCH FANCA MSH6 MSH6 6 MLH1 HOXB13 CDH1 RET FANCB PMS2 PMS2 7 MSH2 TP53 CDK4 SDHAF2 FANCC EPCAM EPCAM 8 MSH6 PTEN CDKN2A SDHB FANCD2 MUTYH MUTYH 9 MUTYH SMAD4 CHEK2 SDHC FANCE APC APC 10 PALB2 STK11 EPCAM SDHD FANCF CDKN2A CDKN2A 11 PMS2 MLH1 TMEM127 FANCG PALB2 PALB2 12 MSH2 VHL FANCI SMAD4 SMAD4 13 MSH6 FANCL BMPR1A BMPR1A 14 MUTYH FANCM TP53 TP53 15 TP53 KRAS PTEN PTEN 16 PALB2 MLH3 STK11 STK11 17 PMS2 MRE11 CDH1 CDH1 18 PTEN NBS1 NBN1 NBN1 19 SMAD4 PIK3CA CHEK2 CHEK2 20 STK11 PMS1 RAD51C RAD51C 21 RAD50 RAD51D RAD51D 22 RAD51C BRIP1 BRIP1 23 BARD1 BARD1 24 ATM ATM 25 CDK4 CDK4 26 MITF 27 ELAC2

TABLE 2C Panel O Gene # Gene Symbol 1 BRCA1 2 BRCA2 3 MLH1 4 MSH2 5 PMS2 6 MLH3 7 EPCAM 8 MSH6 9 APC 10 PMS1 11 PTEN 12 STK11 13 RET 14 SDHD 15 SDHC 16 SDHB 17 SDHAF2 18 CDH1 19 MUTYH 20 SMAD4 21 MEN1 22 VHL 23 BMPR1A 24 PALB2 25 TP53 26 FANCL 27 BLM 28 CDK4 29 CDKN2A 30 ATM 31 PTCH1 32 CHEK2 33 RAD51C 34 CEBPA 35 NBS1 36 FANCA 37 FANCC 38 FANCD2 39 FANCE 40 FANCG 41 FANCI 42 FANCM 43 RAD51D 44 FANCF 45 FANCB 46 BARD1 47 RAD50 48 MRE11 49 BRIP1 50 FLCN 51 TMEM127 52 PIK3CA 53 KRAS 54 BRAF 55 HOXB13 56 ATR 57 BAP1 58 CFTR 59 CTRC 60 FGFR2 61 FH 62 HRAS 63 KITLG 64 NF1 65 NF2 66 PRSS1 67 RB1 68 SPINK1 69 TGFB2

TABLE 2D Panel P Gene # Gene Symbol 1 BRCA1 2 BRCA2 3 MLH1 4 MSH2 5 MSH6 6 PMS2 7 EPCAM 8 APC 9 MUTYH 10 PALB2 11 CDKN2A 12 CDK4 13 TP53 14 PTEN 15 CDH1 16 STK11 17 SMAD4 18 BMPR1A 19 ATM 20 CHEK2 21 RAD51C 22 RAD51D 23 MLH3 24 VHL 25 MEN1 26 RET 27 NF1 28 NF2 29 RB1 30 PTCH1 31 FH 32 BLM 33 CEBPA 34 FLCN 35 SDHB 36 SDHC 37 SDHD 38 SDHAF2 39 TMEM127 40 CFTR 41 PRSS1 42 CTRC 43 SPINK1 44 KRAS 45 BRIP1 46 BARD1 47 NBS1 48 RAD50 49 FANCA 50 FANCB 51 FANCC 52 FANCD2 53 FANCE 54 FANCF 55 FANCG 56 FANCI 57 FANCL 58 FANCM 59 ATR 60 HRAS 61 TGFB2 62 FGFR2 63 BAP1 64 KITLG 65 BRAF 66 MRE11 67 PIK3CA 68 PMS1 69 HOXB13

In some embodiments of each of the aspects of the present disclosure determining (or estimating) the subject's risk for developing cancer comprises determining (or estimating) the subject's percent probability of developing cancer within one or more specific periods of time. In some such embodiments, the period(s) of time may be the next five years; a plurality of five-, ten-, 15-, or 20-year intervals; the remainder of the subject's expected lifetime; any time before a specific age (e.g., 50, 55, 60, 65, 70, 75, 80, 85, 90 years of age); etc.

In some embodiments “analyzing a sample of DNA obtained or derived from a subject to detect the subject's genotype for a plurality of genomic loci (e.g., at most Xgenomic loci) comprising a plurality of test loci comprising at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, or more of the loci listed in Table 1, and/or for one or more of such test genomic locus a locus in linkage disequilibrium with one or more thereof” comprises subjecting the sample to next-generation sequencing. In some such embodiments such sequencing comprises sequencing genomic regions flanking each genomic locus and/or each test locus. In some embodiments such regions include at least 5, 10, 15, 20, 30, 40, 50, 60, 70, 80, 90, 100, 125, 150, 175, 200, 250, 300, 350, 400, 450, or 500 nucleotides on one or both sides of the locus. In some embodiments such analyzing comprises subjecting the sample to microarray analysis. In some such embodiments the microarray comprises probes specific for a plurality of test genomic loci, wherein the plurality consists of at most X genomic loci and comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, or more test loci listed in Table 1, and/or for one or more of such test genomic locus a locus in linkage disequilibrium with one or more thereof.

In some embodiments of each of the aspects of the present disclosure the risk of developing cancer is determined (or estimated) for breast cancer. In some embodiments the subject has not had any previous cancer. In some embodiments the subject has not had breast cancer, lobular carcinoma or ductal carcinoma.

The risk conferred by any particular test locus may be affected by the ethnicity of the subject. Thus, in some embodiments of each of the aspects of the present disclosure the subject's race is Caucasian.

In some embodiments of each of the aspects of the present disclosure a second locus is in linkage disequilibrium with a first locus (e.g., a test locus in Table 1) if such second locus has linkage disequilibrium above 0.9 or 1.

In some embodiments of each of the aspects of the present disclosure the sample is, or is derived from, a bodily fluid. In some embodiments, the sample is, or is derived from, known or expected germline tissue (e.g., nucleated blood cells, fibroblasts, etc.).

In some embodiments of each of the aspects of the present disclosure screening for breast cancer comprises performing a clinical breast exam, mammogram, breast MRI, or enrolling the subject in a screening breast MRIC and mammography program.

In some embodiments of each of the aspects of the present disclosure a particular treatment modality is administered, recommended or prescribed in a subject whose risk of developing breast cancer (e.g., in the next five years, for the remainder of the subject life expectancy, before a certain age, etc.) is at least 15%, 20%, 25%, 30%, 35%, 40%, 50%, 60%, 70%, 80%, or 90%. In some embodiments the treatment modality is breast cancer screening. In some embodiments the treatment modality is an anti-cancer therapy (e.g., medical management that includes some action meant to prevent cancer). In some embodiments such anti-cancer therapy is administered, recommended or prescribed in a subject whose risk of developing breast cancer over the subsequent five years is greater than about 1.66%. In some embodiments the anti-cancer therapy comprises estrogen receptor therapy.

In some embodiments of each of the above aspects of the present disclosure, “analyzing DNA” can comprise either (a) analyzing (e.g., informatically) DNA data previously obtained from a patient sample or (b) obtaining DNA from a patient sample and performing a laboratory assay to obtain data regarding such DNA. Analogously, “determining the nucleotide sequence of” DNA includes either (a) analyzing (e.g., informatically) DNA sequence data previously obtained from a patient sample or (b) obtaining DNA from a patient sample and performing a laboratory assay to sequence such DNA.

Detection of Test Locus Genotypes

The present disclosure generally relates to the detection of genotypes for test loci. The methodology or technique for preparing nucleic acids in a form that is suitable for genotype detection can include, but are not limited to, PCR, detectable probes, sequencing and single base extensions, reverse transcriptase-PCR (RT-PCR), real-time PCR, allele-specific hybridization, reverse transcription quantitative real-time PCR (RT-qPCR) ligase chain reaction, strand displacement amplification (SDA), self-sustained sequence replication (3SR), or in situ PCR. Exemplary, but non-limiting, techniques for analysis of nucleic acid samples to detect test locus variants are briefly described below. One preferred technique is NGS.

DNA Sequencing and Single Base Extensions

Genotypes can also be detected by direct sequencing. Techniques include e.g., dideoxy sequencing-based methods and other methods such as Maxam and Gilbert sequence (see, e.g., Sambrook et al., supra).

Other detection techniques include Pyrosequencing™ of oligonucleotide-length products. Such techniques often employ amplification techniques such as PCR. For example, in pyrosequencing, a sequencing primer is hybridized to a single stranded, PCR-amplified, DNA template; and incubated with the enzymes, DNA polymerase, ATP sulfurylase, luciferase and apyrase, and the substrates, adenosine 5′ phosphosulfate (APS) and luciferin. The first of four deoxynucleotide triphosphates (dNTP) is added to the reaction. DNA polymerase catalyzes the incorporation of the deoxynucleotide triphosphate into the DNA strand, if it is complementary to the base in the template strand. Each incorporation event is accompanied by release of pyrophosphate (PPi) in a quantity equimolar to the amount of incorporated nucleotide. ATP sulfurylase quantitatively converts PPi to ATP in the presence of adenosine 5′ phosphosulfate. This ATP drives the luciferase-mediated conversion of luciferin to oxyluciferin that generates visible light in amounts that are proportional to the amount of ATP. The light produced in the luciferase-catalyzed reaction is detected by a charge coupled device (CCD) camera and seen as a peak in a Pyrogram™. Each light signal is proportional to the number of nucleotides incorporated. Apyrase, a nucleotide degrading enzyme, continuously degrades unincorporated dNTPs and excess ATP. When degradation is complete, another dNTP is added.

Another similar technique for detecting genotypes does not require use of a complete PCR, but typically uses only the extension of a primer by a single, fluorescence-labeled dideoxyribonucleic acid molecule (ddNTP) that is complementary to the nucleotide to be investigated. The nucleotide at the polymorphic site can be identified via detection of a primer that has been extended by one base and is fluorescently labeled (e.g., Kobayashi et al, Mol. Cell. Probes, 9:175-182, 1995).

Distinguishing Test Locus Genotypes

Amplification products can be analyzed using techniques including, without limitation, electrophoretic analysis or sequence analysis. Non-limiting examples of electrophoretic analysis include slab gel electrophoresis such as agarose or polyacrylamide gel electrophoresis, capillary electrophoresis, and denaturing gradient gel electrophoresis (DGGE). Other methods of nucleic acid analysis include, but are limited to, hybridization with allele-specific oligonucleotide probes (Wallace et al., Nucl. Acids Res. (1978) 6:3543-3557), including immobilized oligonucleotides (Saiki et al., PNAS (1989) 86:6230-6234), oligonucleotide arrays (Maskos and Southern, Nucl. Acids Res. (1993) 21:2269-2270), oligonucleotide-ligation assay (OLA) (Landegren et al., Science (1988) 241:1077), allele-specific ligation chain reaction (LCR) (Barrany, PNAS (1991) 88:189-193), gap-LCR (Abavaya et al. Nucl. Acids Res. (1995) 23:675-682), single-strand-conformation-polymorphism detection (Orita et al., Genomics (1983) 5:874-879), RNAase cleavage at mismatched base-pairs (Myers et al., Science (1985) 230:1242), genetic bit analysis (GBA) (Nikiforov et al., Nucl. Acids Res. (1994) 22:4167-4175), in situ hybridization, denaturing high performance liquid chromatography (DHPLC) (Kim et al., Genetic Testing (2008) 12:295-298). Non-limiting examples of sequence analysis include NGS (e.g., Chen et al., Genome Res. (2008) 18:1143-1149); Srivatsan et al., PLoS Genet. (2008) 4:e1000139), Maxam-Gilbert sequencing, Sanger sequencing, capillary array DNA sequencing, thermal cycle sequencing (Sears et al., Biotechniques (1992) 13:626-633), solid-phase sequencing (Zimmerman et al., Methods Mol. Cell Biol. (1992) 3:39-42), sequencing with mass spectrometry such as matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF/MS; Fu et al., Nat. Biotechnol. (1998) 16:381-384), sequencing by hybridization (Chee et al., Science (1996) 274:610-614); Drmanac et al., Science (1993) 260:1649-1652); Drmanac et al., Nat. Biotechnol. (1998) 16:54-58), Polony sequencing (Porreca et al., Curr. Protoc. Mol. Biol. (2006) Chp. 7; Unit7.8), ion semiconductor sequencing (Elliott et al., J. Biomol. Tech. 1:24-30 (2010), DNA nanoball sequencing (Kaji et al., Chem. Soc. Rev. (2010) 39:948-56), single molecule real-time sequencing (Flusberg et al., Nat. Methods (2010) 6:461-5), or nanopore DNA sequencing (Wanunu, Phys. Life Rev. (2012)9:125-58).

Allele-Specific Hybridization

This technique, also commonly referred to as allele specific oligonucleotide hybridization (ASO) (e.g., Stoneking et al., AM. J. HUM. GENET. (1991) 48:70-382; Saiki et al., NATURE (1986) 324, 163-166; EP 235,726; and WO/1989/011548), relies on distinguishing between two DNA molecules differing by one base by hybridizing an oligonucleotide probe that is specific for one of the variants to an amplified product obtained from amplifying the nucleic acid sample. This method typically employs short oligonucleotides, e.g., 15-20 bases in length. The probes are designed to differentially hybridize to one variant (e.g., from a reference sequence) versus another. Hybridization conditions should be sufficiently stringent that there is a significant difference in hybridization intensity between alleles, and producing an essentially binary response, whereby a probe hybridizes to only one of the alleles. Some probes are designed to hybridize to a segment of target DNA such that the polymorphic site aligns with a central position (e.g., in a 15-base oligonucleotide at the 7 position; in a 16-based oligonucleotide at either the 8 or 9 position) of the probe, but this design is not required.

The amount and/or presence of an allele is determined by measuring the amount of allele-specific oligonucleotide that is hybridized to the sample. Typically, the oligonucleotide comprises a label (e.g., a fluorescent label). For example, an allele-specific oligonucleotide is applied to immobilized oligonucleotides representing sequences with different nucleotides at the test locus. After stringent hybridization and washing conditions, fluorescence intensity is measured for each variant-specific oligonucleotide.

Suitable assay formats for detecting hybrids formed between probes and target nucleic acid sequences in a sample include, but are not limited to, the immobilized target (dot-blot) format and immobilized probe (reverse dot-blot or line-blot) assay formats. Dot blot and reverse dot blot assay formats are described in U.S. Pat. Nos. 5,310,893; 5,451,512; 5,468,613; and 5,604,099; each incorporated herein by reference.

Detectable Probes

Hybridization probes useful in the methods, systems and kits of the present disclosure can include an allele-specific probe that discriminates between specific alleles of the test locus (e.g., hybridizes to the risk modifying allele but not to the non-risk modifying allele under certain hybridization conditions). The probes can be at least about 12, 15, 16, 18, 20, 22, 24, 25, 30 or more nucleotide fragments of a contiguous sequence surrounding the test locus. The probes can be produced by, for example, chemical synthesis, PCR amplification, generation from longer polynucleotides using restriction enzymes, or other techniques. The probes can be made completely complementary to the target nucleic acid or portion thereof (e.g., to all or a portion of a sequence encoding a target). Therefore, usually high stringency conditions are desirable in order to prevent or at least minimize false positives. However, conditions of high stringency may be best suited to situations where the probes are complementary to regions of the target which lack heterogeneity. The stringency of hybridization is determined by a number of factors during hybridization and during the washing procedure, including temperature, ionic strength, length of time, and concentration of formamide (Sambrook et al. (1989), “Molecular Cloning; A Laboratory Manual,” Second Edition (Cold Spring Harbor Press, Cold Spring Harbor, N.Y.)).

Nucleic acid probes, or alternatively nucleic acid from the samples, can be provided in solution for such assays, or can be affixed to a support (e.g., solid or semi-solid support). Examples of supports that can be used are nitrocellulose (e.g., in membrane or microtiter well form), polyvinyl chloride (e.g., in sheets or microtiter wells), polystyrene latex (e.g., in beads or microtiter plates, polyvinylidine fluoride, diazotized paper, nylon membranes, activated beads, and Protein A beads.

Probes detectable upon a secondary structural change are also suitable for detection of a test locus genotype. Exemplified secondary structure or stem-loop structure probes include molecular beacons or Scorpion® primer/probes. Molecular beacon probes are single-stranded oligonucleic acid probes that can form a hairpin structure in which a fluorophore and a quencher are usually placed on the opposite ends of the oligonucleotide. At either end of the probe short complementary sequences allow for the formation of an intra molecular stem, which enables the fluorophore and the quencher to come into close proximity. The loop portion of the molecular beacon is complementary to a target nucleic acid of interest. Binding of this probe to its target nucleic acid of interest forms a hybrid that forces the stem apart. This causes a conformation change that moves the fluorophore and the quencher away from each other and leads to a more intense fluorescent signal. See, e.g., Tyagi & Kramer, Nat. Biotechnol. (1996) 14:303-308; Tyagi et al., Nat. Biotechnol. (1998) 16:49-53; Piatek et al., Nat. Biotechnol. (1998) 16:359-363; Marras et al., Genetic Analysis: Biomolecular Engineering (1999) 14:151-156; Tpp et al, BioTechniques (2000) 28:732-738).

Clinical Management

The present disclosure also provides methods of administering, recommending, prescribing, etc. specific treatment modalities to subjects whose risk is determined as disclosed herein. These embodiments of the present disclosure thus will provide patient-specific biological information, which will be informative for treatment modality selection.

Reference Standards for Treatment

In many embodiments, the a patient's risk (or percent probability) of developing cancer is compared to a reference (“reference standard” or “reference level”) in order to direct treatment decisions. Such risk can be derived from or embodied in a score. The reference standard used for any embodiment disclosed herein may comprise average, mean, or median scores or risks in a control population. The reference standard may further include an earlier time point for the same subject. For example, a reference standard may include a first time point, and risk can be examined again at second, third, fourth, fifth, sixth time points, etc. Any time point earlier than any particular time point can be considered a reference standard. Because the subject's germline genotype at any test locus is not expected to change over time, however, these time-dependent risks are generally expected to reflect changes in clinical characteristics (e.g., a different Tyrer-Cuzick score due to changed family or personal history). The reference standard may additionally comprise cutoff values or any other statistical attribute of the control population, or earlier time points of the same subject, such as a standard deviation from the mean score or risk. In some embodiments, the control population may comprise healthy individuals, cancer patients having a particular response profile, or the same test patient prior to the administration of any or a specific therapy.

Reference Therapy for Treatment

In some embodiments, a test subject is treated more or less aggressively than a reference therapy based on the difference between the test subject's risk (or genotypes) and the reference risk (or genotypes). In some embodiments a reference therapy is any therapy that is the standard of care for a reference subject of average risk for developing cancer. The standard of care can vary temporally and geographically, and a skilled person can determine the appropriate standard of care by consulting the relevant medical literature.

In some embodiments, a more aggressive therapy than the standard therapy comprises beginning treatment earlier than in the standard therapy. In some embodiments, a more aggressive therapy than the standard therapy comprises administering additional treatments beyond the standard therapy. In some embodiments, a more aggressive therapy than the standard therapy comprises administering alternative treatments instead of the standard therapy. In some embodiments, a more aggressive therapy than the standard therapy comprises treating on an accelerated schedule compared to the standard therapy (e.g., earlier initiation of mammograms or breast MRIs). In one embodiment a more aggressive therapy comprises increased frequency of the administration schedule (e.g., more frequent mammograms or breast MRIs).

Kits

In some aspects and embodiment the present disclosure describes novel kits. In some embodiments the kits of the present disclosure comprise detection reagents packaged together in the form of a kit for conducting any of the assays disclosed herein. In certain embodiments, the kits comprise oligonucleotides capable of specifically detecting one or more test loci genotypes (or alleles) as described herein. The oligonucleotide sequences may correspond to fragments of the biomarker nucleic acids. For example, the oligonucleotides can be more than 200, 200, 150, 100, 50, 25, 10, or fewer than 10 nucleotides in length. The kit can contain in separate containers a solution of nucleic acids, control formulations (positive and/or negative), and/or a detectable label, such as but not limited to fluorescein, green fluorescent protein, rhodamine, cyanine dyes, Alexa dyes, luciferase, and radiolabels, among others. Instructions for carrying out the assay can optionally be included in the kit.

In other embodiments of the present disclosure, the kit can contain a nucleic acid substrate array comprising one or more nucleic acid sequences. Such an array may comprise oligonucleotides capable of specifically detecting one or more test locus genotypes (or alleles) as described herein. In various embodiments, the presence or absence of one or more of the test locus alleles can be identified by virtue of binding to the array. In some embodiments the substrate array can be on a solid substrate, such as what is known as a “chip.” See, e.g., U.S. Pat. No. 5,744,305. In some embodiments the substrate array can be a solution array; e.g., xMAP (Luminex, Austin, Tex.), Cyvera (Illumina, San Diego, Calif.), RayBio Antibody Arrays (RayBiotech, Inc., Norcross, Ga.), CellCard (Vitra Bioscience, Mountain View, Calif.) and Quantum Dots' Mosaic (Invitrogen, Carlsbad, Calif.).

Systems, Computer-Related and Computer-Implemented Aspects of the Disclosure

The results of any analyses according to the disclosure will often be communicated to physicians, genetic counselors and/or patients (or other interested parties such as researchers) in a transmittable form that can be communicated or transmitted to any of the above parties. Such a form can vary and can be tangible or intangible (e.g., electronic). The results can be embodied in descriptive statements, diagrams, photographs, charts, images or any other visual forms. For example, graphs showing expression or activity level or sequence variation information for various genes can be used in explaining the results. Diagrams showing such information for additional target gene(s) are also useful in indicating some testing results. The statements and visual forms can be recorded on a tangible medium such as papers, computer readable media such as floppy disks, compact disks, etc., or on an intangible medium, e.g., an electronic medium in the form of email or website on internet or intranet. In addition, results can also be recorded in a sound form and transmitted through any suitable medium, e.g., analog or digital cable lines, fiber optic cables, etc., via telephone, facsimile, wireless mobile phone, internet phone and the like.

Thus, the information and data on a test result can be produced anywhere in the world and transmitted to a different location. As an illustrative example, when a sequencing (or genotyping) assay is conducted outside the United States, the information and data on a test result may be generated, cast in a transmittable form as described above, and then imported into the United States. Accordingly, the present disclosure also encompasses methods and systems for producing a transmittable form of sequence information for at least one patient sample. The method comprises the steps of (1) sequencing nucleic acids in a sample according to methods of the present disclosure; and (2) embodying the result of the sequencing step in a transmittable form. The transmittable form is a product of such a method.

Techniques for analyzing sequence data (indeed any data obtained according to the disclosure) may be implemented using hardware, software or a combination thereof in one or more computer systems or other processing systems capable of effectuating such analysis.

The sample analyzer in the systems of the disclosure can be any instrument useful in sequencing nucleic acids, including but not limited to, Illumina HiSeq™, Ion Torrent PGM, ABI SOLiD™ sequencer, PacBio RS, Helicos Heliscope™, or any instrument utilizing a sequencing system discussed above.

The computer-based analysis function can be implemented in any suitable language and/or browsers. For example, it may be implemented with C language and preferably using object-oriented high-level programming languages such as Visual Basic, SmallTalk, C++, and the like. The application can be written to suit environments such as the Microsoft Windows™ environment including Windows™ 98, Windows™ 2000, Windows™ NT, and the like. In addition, the application can also be written for the Macintosh™, SUN™, UNIX or LINUX environment. In addition, the functional steps can also be implemented using a universal or platform-independent programming language. Examples of such multi-platform programming languages include, but are not limited to, hypertext markup language (HTML), JAVA™, JavaScript™, Flash programming language, common gateway interface/structured query language (CGI/SQL), practical extraction report language (PERL), AppleScript™ and other system script languages, programming language/structured query language (PL/SQL), and the like. Java™ or JavaScript™-enabled browsers such as HotJava™, Microsoft™ Explorer™, or Netscape™ can be used. When active content web pages are used, they may include Java™ applets or ActiveX™ controls or other active content technologies.

The analysis function can also be embodied in computer program products and used in the systems described above or other computer- or internet-based systems. Accordingly, another aspect of the present disclosure relates to a computer program product comprising a computer-usable medium having computer-readable program codes or instructions embodied thereon for enabling a processor to carry out gene status analysis. These computer program instructions may be loaded onto a computer or other programmable apparatus to produce a machine, such that the instructions which execute on the computer or other programmable apparatus create means for implementing the functions or steps described above. These computer program instructions may also be stored in a computer-readable memory or medium that can direct a computer or other programmable apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory or medium produce an article of manufacture including instruction means which implement the analysis. The computer program instructions may also be loaded onto a computer or other programmable apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions or steps described above.

One example of a computer system of the disclosure is the computer system [400] illustrated in FIG. 4. Computer system [400] may include at least one input module [430] for entering patient data into the computer system [400]. The computer system [400] may include at least one output module [424] for indicating whether a patient has an increased or decreased likelihood of response and/or indicating suggested treatments determined by the computer system [400]. Computer system [400] may include at least one memory module [406] in communication with the at least one input module [430] and the at least one output module [424].

The at least one memory module [406] may include, e.g., a removable storage drive [408], which can be in various forms, including but not limited to, a magnetic tape drive, a floppy disk drive, a VCD drive, a DVD drive, an optical disk drive, etc. The removable storage drive [408] may be compatible with a removable storage unit [410] such that it can read from and/or write to the removable storage unit [410]. Removable storage unit [410] may include a computer usable storage medium having stored therein computer-readable program codes or instructions and/or computer readable data. For example, removable storage unit [410] may store patient data. Example of removable storage unit [410] are well known in the art, including, but not limited to, floppy disks, magnetic tapes, optical disks, and the like. The at least one memory module [406] may also include a hard disk drive [412], which can be used to store computer readable program codes or instructions, and/or computer readable data.

In addition, as shown in FIG. 2, the at least one memory module [406] may further include an interface [414] and a removable storage unit [416] that is compatible with interface [414] such that software, computer readable codes or instructions can be transferred from the removable storage unit [416] into computer system [400]. Examples of interface [414] and removable storage unit [416] pairs include, e.g., removable memory chips (e.g., EPROMs or PROMs) and sockets associated therewith, program cartridges and cartridge interface, and the like. Computer system [400] may also include a secondary memory module [418], such as random access memory (RAM).

Computer system [400] may include at least one processor module [402]. It should be understood that the at least one processor module [402] may consist of any number of devices. The at least one processor module [402] may include a data processing device, such as a microprocessor or microcontroller or a central processing unit. The at least one processor module [402] may include another logic device such as a DMA (Direct Memory Access) processor, an integrated communication processor device, a custom VLSI (Very Large Scale Integration) device or an ASIC (Application Specific Integrated Circuit) device. In addition, the at least one processor module [402] may include any other type of analog or digital circuitry that is designed to perform the processing functions described herein.

As shown in FIG. 4, in computer system [400], the at least one memory module [404], the at least one processor module [402], and secondary memory module [418] are all operably linked together through communication infrastructure [420], which may be a communications bus, system board, cross-bar, etc.). Through the communication infrastructure [420], computer program codes or instructions or computer readable data can be transferred and exchanged. Input interface [426] may operably connect the at least one input module [426] to the communication infrastructure [420]. Likewise, output interface [422] may operably connect the at least one output module [424] to the communication infrastructure [420].

The at least one input module [430] may include, for example, a keyboard, mouse, touch screen, scanner, and other input devices known in the art. The at least one output module [424] may include, for example, a display screen, such as a computer monitor, TV monitor, or the touch screen of the at least one input module [430]; a printer; and audio speakers. Computer system [400] may also include, modems, communication ports, network cards such as Ethernet cards, and newly developed devices for accessing intranets or the internet.

The at least one memory module [406] may be configured for storing patient data entered via the at least one input module [430] and processed via the at least one processor module [402]. Patient data relevant to the present disclosure may include sequence or variant information for one or more of the genes in any of Panels A-Y. Patient data relevant to the present disclosure may also include clinical parameters relevant to the patient (e.g., age, lifestyle and environmental risk factors for cancer, previously diagnosed diseases (including previously diagnosed cancers), tumor size, node status, tumor stage). Any patient data a physician might find useful in making treatment decisions/recommendations may also be entered into the system, including but not limited to age, gender, and race/ethnicity and lifestyle data such as diet information. Other possible types of patient data include symptoms currently or previously experienced, patient's history of illnesses, medications, and medical procedures.

The at least one memory module [406] may include a computer-implemented method stored therein. The at least one processor module [402] may be used to execute software or computer-readable instruction codes of the computer-implemented method. The computer-implemented method may be configured to, based upon the patient data, indicate whether the patient has an increased likelihood of recurrence, progression or response to any particular treatment, generate a list of possible treatments, etc.

In certain embodiments, the computer-implemented method may be configured to identify a patient as having or not having an increased risk of a particular cancer. For example, the computer-implemented method may be configured to inform a physician that a particular patient has an increased risk of a particular cancer. Alternatively or additionally, the computer-implemented method may be configured to actually suggest a particular course of treatment based on the answers to/results for various queries.

FIG. 5 illustrates one embodiment of a computer-implemented method [500] of the disclosure that may be implemented with the computer system [400] of the disclosure. The method [500] begins with a query [510]. If the answer to/result for this query is “Yes” [520], then the method may diagnose [530] the patient as having an increased risk of a particular cancer (e.g., breast cancer if there is a germline deficiency in BRCA1). If the answer to/result for this query is “No” [511], then the method may perform another query [512]. If the answer to/result for this query is “Yes” [521], then the method may diagnose [531] the patient as having an increased risk of cancer. The method [500] may then proceed with more queries, make a particular treatment recommendation ([540], [541]), or simply end.

Though the queries are performed sequentially as suggested in FIG. 5, the laboratory analysis that provides data to answer these queries may be performed in any suitable order. Optionally, the method may query clinical parameters (e.g., age of menarche) before or after querying any of the molecular characteristics of cancer predisposition genes or test locus genotypes as shown. As mentioned above, the preceding order of queries may be modified. In some embodiments an answer of “yes” to one query (e.g., [510]) prompts one or more of the remaining queries to confirm that the patient has, e.g., increased risk of developing cancer.

In some embodiments, the computer-implemented method of the disclosure [500] is open-ended. In other words, the apparent first step [510] in FIG. 5 may actually form part of a larger process and, within this larger process, need not be the first step/query. Additional steps may also be added onto the core methods discussed above. These additional steps include, but are not limited to, informing a health care professional (or the patient itself) of the diagnosis reached; combining the conclusion reached by the illustrated method [500] with other facts or conclusions to reach some additional or refined conclusion regarding the patient's diagnosis, prognosis, treatment, etc.; making a recommendation for treatment (e.g., “patient should/should not undergo earlier mammogram”); additional queries about additional biomarkers, clinical parameters, or other useful patient information (e.g., age, general patient health, etc.).

Regarding the above computer-implemented method [500], the answers to the queries may be determined by the method instituting a search of patient data for the answer. For example, to answer the respective queries ([510], [512]), patient data may be searched for germline sequence data for the cancer predisposition genes to be analyzed (e.g., two or more of the genes in Panel B or Panel N) and/or for genotype data for the test loci to be analyzed. The queries may be performed in no particular order or according to some desired order. If such a comparison has not already been performed, the method may compare these data to some reference (e.g., reference sequence) in order to determine if the patient has a germline deficiency in any of the cancer predisposition genes being analyzed. Additionally or alternatively, the method may present one or more of the queries ([510], [512]) to a user of the computer system [400] (e.g., a physician). For example, the questions ([510], [512]) may be presented via an output module [424]. The user may then answer “Yes” or “No” or provide some other value (e.g., test score) via an input module [430]. The method may then proceed based upon the answer received. Likewise, the conclusions ([530], [531]) may be presented to a user of the computer-implemented method via an output module [424].

The practice of the present disclosure may also employ conventional biology methods, software and systems. Computer software products of the disclosure typically include computer readable media having computer-executable instructions for performing the logic steps of the method of the disclosure. Suitable computer readable medium include floppy disk, CD-ROM/DVD/DVD-ROM, hard-disk drive, flash memory, ROM/RAM, magnetic tapes and etc. Basic computational biology methods are described in, for example, Setubal et al., INTRODUCTION TO COMPUTATIONAL BIOLOGY METHODS (PWS Publishing Company, Boston, 1997); Salzberg et al. (Ed.), COMPUTATIONAL METHODS IN MOLECULAR BIOLOGY, (Elsevier, Amsterdam, 1998); Rashidi & Buehler, BIOINFORMATICS BASICS: APPLICATION IN BIOLOGICAL SCIENCE AND MEDICINE (CRC Press, London, 2000); and Ouelette & Bzevanis, BIOINFORMATICS: A PRACTICAL GUIDE FOR ANALYSIS OF GENE AND PROTEINS (Wiley & Sons, Inc., 2^(nd) ed., 2001); see also, U.S. Pat. No. 6,420,108.

The present disclosure may also make use of various computer program products and software for a variety of purposes, such as probe design, management of data, analysis, and instrument operation. See U.S. Pat. Nos. 5,593,839; 5,795,716; 5,733,729; 5,974,164; 6,066,454; 6,090,555; 6,185,561; 6,188,783; 6,223,127; 6,229,911 and 6,308,170. Additionally, the present disclosure may have embodiments that include methods for providing genetic information over networks such as the Internet as shown in U.S. Ser. No. 10/197,621 (U.S. Pub. No. 20030097222); Ser. No. 10/063,559 (U.S. Pub. No. 20020183936), Ser. No. 10/065,856 (U.S. Pub. No. 20030100995); Ser. No. 10/065,868 (U.S. Pub. No. 20030120432); Ser. No. 10/423,403 (U.S. Pub. No. 20040049354).

A machine-readable storage medium can comprise, for example, a data storage material that is encoded with machine-readable data or data arrays. The data and machine-readable storage medium are capable of being used for a variety of purposes, when using a machine programmed with instructions for using said data. Such purposes include, without limitation, storing, accessing and manipulating information or data relating to mutation load of a patient or population over time. Data comprising the presence of test locus variants can be implemented in computer programs that are executing on programmable computers, which comprise a processor, a data storage system, one or more input devices, one or more output devices, etc. Program code can be applied to the input data to perform the functions described herein, and to generate output information. This output information can then be applied to one or more output devices. The computer can be, for example, a personal computer, a microcomputer, or a workstation of conventional design.

The computer programs can be implemented in a high-level procedural or object-oriented programming language, to communicate with a computer system. The programs can also be implemented in machine or assembly language. The programming language can also be a compiled or interpreted language. Each computer program can be stored on storage media or a device such as ROM, magnetic diskette, etc., and can be readable by a programmable computer for configuring and operating the computer when the storage media or device is read by the computer to perform the described procedures. Any health-related data management systems of the present disclosure can be considered to be implemented as a computer-readable storage medium, configured with a computer program, where the storage medium causes a computer to operate in a specific manner to perform various functions, as described herein.

The assays disclosed herein can be used to generate a “genotype profile.” The test genotype profile can then be compared to a reference genotype profile. The biomarker profiles, reference and test, of embodiments of the present disclosure can be contained in a machine-readable medium, such as analog tapes like those readable by a CD-ROM or USB flash media, among others. The machine-readable media can also comprise subject information; e.g., the subject's medical or family history.

ADDITIONAL EMBODIMENTS

Embodiment 1. A method for genotyping a subject comprising: analyzing a sample of DNA obtained or derived from the subject to detect the genotype fora plurality of genomic loci, wherein the plurality consists of at most X loci and comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, or more test loci listed in Table 1, and/or for one or more of such test locus a locus in linkage disequilibrium with one or more thereof.

Embodiment 2. A method for assessing risk for developing cancer comprising:

-   -   (1) analyzing a sample of DNA obtained or derived from a subject         to detect the subject's genotype for a plurality of genomic loci         comprising at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,         15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70,         75, 80, or more of the test loci listed in Table 1, and/or for         one or more of such test locus a locus in linkage disequilibrium         with one or more thereof;     -   (2) determining (or estimating) the subject's risk for         developing cancer based at least in part on the genotypes         detected in (2).

Embodiment 3. A method for assessing risk for developing cancer comprising:

-   -   (1) analyzing a sample of DNA obtained or derived from a subject         to detect the subject's genotype for a plurality of genomic loci         comprising at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,         15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70,         75, 80, or more of the test loci listed in Table 1, and/or for         one or more of such test locus a locus in linkage disequilibrium         with one or more thereof;     -   (2) calculating a test score incorporating (or derived from) the         genotypes detected in (1); and     -   (3)(a) diagnosing a subject for whom the score in (2) exceeds a         reference score as having a test likelihood of developing cancer         that is higher than a reference likelihood of developing cancer;         or     -   (3)(b) diagnosing a subject for whom the score in (2) does not         exceed a reference score as having a test likelihood of         developing cancer that is equal to or lower than a reference         likelihood of developing cancer.

Embodiment 4. A method for treating a subject comprising:

-   -   (1) determining the subject's genotype for a plurality of         genomic loci comprising at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,         12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55,         60, 65, 70, 75, 80, or more of the test loci listed in Table 1,         and/or for one or more of such test locus a locus in linkage         disequilibrium with one or more thereof;     -   (2) determining (or estimating) the subject's risk for         developing cancer based at least in part on the genotypes         determined in (1);     -   (3) prescribing or administering one or more treatment         modalities indicated based at least in part on the risk         determined (or estimated) in (2).

Embodiment 5. A kit for genotyping a subject comprising: a compartmentalized container; reagents for analyzing a sample of DNA obtained or derived from the subject to detect the genotype for a plurality of genomic loci, wherein the plurality consists of at most X loci and comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, or more test loci listed in Table 1, and/or for one or more of such test genomic locus a locus in linkage disequilibrium with one or more thereof; and instructions for use of the reagents.

Embodiment 6. A system for assessing risk for genotyping a subject, comprising:

-   -   (1) a sample analyzer for analyzing DNA in, extracted from or         derived from a sample of the subject to detect the subject's         genotype for a plurality of genomic loci comprising at least 2,         3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,         25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, or more of the         test loci listed in Table 1, and/or for one or more of such test         locus a locus in linkage disequilibrium with one or more         thereof, wherein the sample analyzer contains (a) the         sample, (b) genomic DNA from the sample, (c) transcript RNA from         the sample, or (d) DNA derived (e.g., synthesized or amplified)         from said genomic DNA; and     -   (2) one or more computer programs for performing any one, all,         or any combination of the following functions: (a) a computer         program for querying the subject's genotype data; (b)         calculating the subject's risk for developing cancer based at         least on the subject's genotype data; (c) calculating a test         score based on or derived from the subject's genotype data; (d)         comparing the test score to one or more reference scores; (e)         concluding whether there is an increased risk of cancer based at         least in part on the comparison in (d); and (f) outputting         (e.g., displaying) the conclusion in (e).

Embodiment 7. A computer program product comprising a computer-usable medium comprising:

-   -   (1) a computer program for receiving, storing, and/or retrieving         a subject's genotype data for a plurality of genomic loci         comprising at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,         15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70,         75, 80, or more of the test loci listed in Table 1, and/or for         one or more of such test locus a locus in linkage disequilibrium         with one or more thereof; and     -   (2) one or more computer programs for performing any one, all,         or any combination of the following functions: (a) a computer         program for querying the subject's genotype data; (b)         calculating the subject's risk for developing cancer based at         least on the subject's genotype data; (c) calculating a test         score based on or derived from the subject's genotype data; (d)         comparing the test score to one or more reference scores; (e)         concluding whether there is an increased risk of cancer based at         least in part on the comparison in (d); and (f) outputting         (e.g., displaying) the conclusion in (e).

Embodiment 8. The method of Embodiment 2 or 4, wherein determining in (2) comprises determining (or estimating) the subject's percent probability of developing cancer within one or more specific periods of time.

Embodiment 9. The method of Embodiment 8, wherein the periods of time are chosen from the next five years; a plurality of five-, ten-, 15-, or 20-year intervals; the remainder of the subject's expected lifetime; or any time before a specific age (e.g., 50, 55, 60, 65, 70, 75, 80, 85, 90 years of age).

Embodiment 10. The method of any one of Embodiments 2, 4, 8, or 9, wherein determining in (2) comprises determining the subject's risk for developing cancer attributable or allocable to the genotypes detected in (1).

Embodiment 11. The method of Embodiment 10, wherein determining in (2) comprises combining the subject's risk for developing cancer attributable or allocable to the genotypes detected in (1) with other risk factors or calculations to yield a composite risk score or estimation.

Embodiment 12. The method of Embodiment 3, further comprising determining (or estimating) the subject's percent probability of developing cancer within one or more specific periods of time.

Embodiment 13. The method of Embodiment 12, wherein the periods of time are chosen from the next five years; a plurality of five-, ten-, 15-, or 20-year intervals; the remainder of the subject's expected lifetime; or any time before a specific age (e.g., 50, 55, 60, 65, 70, 75, 80, 85, 90 years of age).

Embodiment 14. The method of any one of Embodiments 3, 12, or 13, further comprising determining the subject's risk for developing cancer attributable or allocable to the genotypes detected in (1).

Embodiment 15. The method of Embodiment 14, further comprising combining the subject's risk for developing cancer attributable or allocable to the genotypes detected in (1) with other risk factors or calculations to yield a composite risk score or estimation.

Embodiment 16. The system of Embodiment 6 or the computer program product of Embodiment 7, further comprising a computer program for determining (or estimating) the subject's percent probability of developing cancer within one or more specific periods of time.

Embodiment 17. The system or computer program product of Embodiment 16, wherein the periods of time are chosen from the next five years; a plurality of five-, ten-, 15-, or 20-year intervals; the remainder of the subject's expected lifetime; or any time before a specific age (e.g., 50, 55, 60, 65, 70, 75, 80, 85, 90 years of age).

Embodiment 18. The system or computer program product of any one of Embodiments 6, 7, 16, or 17, further comprising a computer program for determining the subject's risk for developing cancer attributable or allocable to the genotypes detected in (1).

Embodiment 19. The system or computer program product of any one of Embodiments 6, 7, 16, 17, or 18, further comprising combining the subject's risk for developing cancer attributable or allocable to the genotypes detected in (1) with other risk factors or calculations to yield a composite risk score or estimation.

Embodiment 20. The method, system, or computer program product of any one of Embodiments 1 to 19, wherein the sample comprises germline genomic DNA of the subject or DNA derived therefrom.

Embodiment 21. The method, system, or computer program product of any one of Embodiments 1 to 19, wherein the subject does not or has been determined to not harbor a pathogenic or likely pathogenic variant in any cancer predisposition gene and the risk determined is the subject's residual risk of developing cancer.

Embodiment 22. The method, system, or computer program product of any one of Embodiments 1 to 19, wherein the subject does not or has been determined to not harbor a pathogenic or likely pathogenic variant in any gene or panel of genes listed in any of Tables 2A, 2B, 2C, or 2D.

Embodiment 23. A method for genotyping a subject comprising:

-   -   (1) analyzing a first sample of DNA obtained or derived from the         subject to detect the sequence for a plurality of test genes         comprising at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,         15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, or more         genes or any panel of genes listed in any of Tables 2A, 2B, 2C,         or 2D; and     -   (2) analyzing either the first sample or a second sample of DNA         obtained or derived from the subject to detect the genotype for         a plurality of genomic loci, wherein the plurality consists of         at most X loci and comprises at least 2, 3, 4, 5, 6, 7, 8, 9,         10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45,         50, 55, 60, 65, 70, 75, 80, or more test loci listed in Table 1,         and/or for one or more of such test locus a locus in linkage         disequilibrium with one or more thereof.

Embodiment 24. A method for assessing risk for developing cancer comprising:

-   -   (1) analyzing a first sample of DNA obtained or derived from the         subject to detect the sequence for a plurality of test genes         comprising at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,         15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, or more         genes or any panel of genes listed in any of Tables 2A, 2B, 2C,         or 2D;     -   (2) analyzing either the first sample or a second sample of DNA         obtained or derived from the subject to detect the genotype for         a plurality of genomic loci, wherein the plurality consists of         at most X loci and comprises at least 2, 3, 4, 5, 6, 7, 8, 9,         10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45,         50, 55, 60, 65, 70, 75, 80, or more test loci listed in Table 1,         and/or for one or more of such test locus a locus in linkage         disequilibrium with one or more thereof; and     -   (3) determining (or estimating) the subject's risk for         developing cancer based at least in part on the genotypes         detected in (2).

Embodiment 25. A method for assessing risk for developing cancer comprising:

-   -   (1) analyzing a first sample of DNA obtained or derived from the         subject to detect the sequence for a plurality of test genes         comprising at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,         15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, or more         genes or any panel of genes listed in any of Tables 2A, 2B, 2C,         or 2D;     -   (2) analyzing either the first sample or a second sample of DNA         obtained or derived from the subject to detect the genotype for         a plurality of genomic loci, wherein the plurality consists of         at most X loci and comprises at least 2, 3, 4, 5, 6, 7, 8, 9,         10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45,         50, 55, 60, 65, 70, 75, 80, or more test loci listed in Table 1,         and/or for one or more of such test locus a locus in linkage         disequilibrium with one or more thereof;     -   (3) calculating a test score incorporating (or derived from) the         genotypes detected in (2); and     -   (4)(a) diagnosing a subject for whom the score in (3) exceeds a         reference score as having a test likelihood of developing cancer         that is higher than a reference likelihood of developing cancer;         or     -   (4)(b) diagnosing a subject for whom the score in (3) does not         exceed a reference score as having a test likelihood of         developing cancer that is equal to or lower than a reference         likelihood of developing cancer.

Embodiment 26. A method for treating a subject comprising:

-   -   (1) determining whether a subject harbors any pathogenic or         likely pathogenic variant in any of a plurality of test genes         comprising at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,         15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, or more         genes or any panel of genes listed in any of Tables 2A, 2B, 2C,         or 2D;     -   (2) determining the subject's genotype for a plurality of         genomic loci comprising at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,         12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55,         60, 65, 70, 75, 80, or more of the test loci listed in Table 1,         and/or for one or more of such test locus a locus in linkage         disequilibrium with one or more thereof;     -   (3) determining (or estimating) the subject's risk for         developing cancer based at least in part on the genotypes         determined in (2);     -   (4) prescribing or administering one or more treatment         modalities indicated based at least in part on the risk         determined (or estimated) in (3).

Embodiment 27. The method of any one of Embodiments 23 to 26, wherein step (2) and any following step or steps is or are performed only in the case where no pathogenic or likely pathogenic vairant is detected in step (1).

Embodiment 28. The method of any one of Embodiments 3, 12, 13, 14, or 15, wherein the test score is calculated according to an equation of the following general formula: Σ_((weighted genotype value for each test locus)), where weighted genotype value for each test locus=(Coefficient)*((Genotype Value)−(Mean Allele Copy Value)).

Embodiment 29. The method of any one of Embodiments 3, 12, 13, 14, or 15, wherein the test score is calculated according to Formula (I).

EXAMPLES Example 1 Background

Women who are unaffected with cancer but have a significant family history of breast cancer are frequently referred for hereditary cancer testing with multi-gene panels. Despite being at high familial risk for development of breast cancer, fewer than 10% of such patients carry a clinically actionable mutation. Large-scale genotyping studies have identified common variants (primarily single-nucleotide polymorphisms, SNPs) that individually confer modest breast cancer risk, but together may explain the genetic susceptibility for breast cancer in many women without monogenic mutations. Here, we describe the development and validation of a polygenic residual risk score (RRS) in a large, consecutive cohort of women who tested negative for mutations in known breast cancer susceptibility genes.

Methods Cohort

A consecutive series of women of European descent who had genetic testing with a multi-gene hereditary cancer risk panel was assessed. Clinical information was obtained from provider-completed test request forms and is summarized in Table A. Only women of European descent were included. The cohort was divided into a training (July-November 2016, N=11,771) and validation set (November 2016-March 2017, N=17,205).

TABLE A Training Set Validation Set All Patients BC Cases All Patients BC Cases (%) (%) (%) (%) Total Patients 11,771 (100) 2,089 (18) 17,205 (100) 2,917 (17) Age at Hereditary Cancer Testing Range 18-84 22-84 18-84 22-84 Median 46 54 47 54 % ≤ 50 61 7 61 7 Ancestry Western/Northern 8,744 (74) 1,661 (80) 12,722 (74) 2,349 (81) European Central/Eastern 2,728 (23) 385 (18) 4,008 (23) 509 (17) European Ashkenazi 299 (3) 43 (2) 475 (3) 59 (2) Cancer History in FDRs No BC or OC 6,367 (54) 1,349 (65) 9,507 (55) 1,919 (66) ≥1 BC 4,262 (36) 642 (31) 6,111 (36) 892 (31) ≥1 OC 1,491 (13) 132 (6) 2,042 (12) 160 (5) ≥2 BC 785 (7) 143 (7) 1,141 (7) 193 (7) ≥2 OC 51 (<1) 6 (<1) 75 (<1) 6 (<1) ≥1 BC and ≥1 OC 349 (3) 34 (2) 455 (3) 54 (2)

Next Generation Sequencing

A screening panel of 94 test loci and 3 loci in linkage disequilibrium with these loci was designed for the RainDance Thunderstorm NGS target enrichment system. Variant calls were obtained by NGS of amplicons on an Illumine HiSeq 2500 next generation sequencer to an average sequence depth of >1200×. Two of the polymorphic loci failed due to their location within repetitive elements and were discarded. Genotyping calls for non-failed 95 variants were validated using Sanger sequencing or the IonTorrent Ampliseq platform in 189 DNA samples. There was 100% concordance between the genotype calls generated using the RainDance Thunderstorm system and the comparator assays.

Statistical Analysis

Multivariable logistic regression models were used to evaluate the variants, develop a RRS as a predictor of personal breast cancer history in the training cohort, and assess the performance of the RRS in the validation cohort. Independent variables included age, personal/family cancer history, and ancestry (West/Northern European, Central/Eastern European, or Ashkenazi). Analysis of the validation cohort was conducted according to a pre-specified Statistical Analysis Plan.

RRS Development Development Methods

SNP genotypes were coded as the number of the effect alleles (0, 1, or 2) (FIG. 1). The corresponding estimated coefficients were combined with log odds ratios (ORs) from the literature using weighted averaging with the weights inversely proportional to the squares of the corresponding confidence intervals. The resulting coefficients β_(i) were used to calculate SNP informativeness defined as 2f_(i)(1−f_(i))β_(i) ², where f_(i) is the effect allele frequency for SNP_(i). To select the optimal number of variants in the final RRS, variants were ordered by informativeness. From 3 groups of linked SNPs in CCND1, ESR1 and TERT, we included only the most informative SNP from each group. Polygenic scores were constructed for cumulatively increasing numbers of variants and were evaluated with log likelihood ratios from logistic regression models.

Development Results

The maximum of the likelihood ratios was achieved with the 82 most informative SNPs (FIG. 2; Panel 4). The final RRS score was defined as RRS=Σβ_(i)n_(i)

-   -   Sum over the 82 most informative SNPs     -   n_(i) is the number of the effect alleles for SNP_(i)     -   β_(i) is the coefficient estimated by combining results from the         logistic regression model with 82 SNPs in the training cohort         with published estimates as described above for 92 variants.

RRS Validation Validation Results

RRS was strongly associated with personal history of breast cancer in the validation cohort (p<10-50). OR per unit standard deviation of the RRS of 1.42 (95% Cl=1.36-1.49). This OR is lower than that of a published polygenic risk score (PRS) based on 77 SNPs (OR 1.55). This difference can be explained by the fact that OR for RRS was adjusted for family cancer history while the published OR for PRS was not.

In a model with both scores included, the RRS score was significantly associated with breast cancer (p=3×10−5) while the PRS was not (p=0.2). FIG. 3 shows the distribution of odds ratios associated with RRS in unaffected women from the validation cohort.

CONCLUSIONS

A residual risk score was developed that is highly predictive of risk of development of future breast cancer in unaffected women with significant family history after testing negative for known high and intermediate risk mutations. The residual risk score significantly outperforms the published polygenic risk score in this population of women due to the inclusion of a larger number of SNPs and the most informative SNPs as well as the refined OR estimates for individual SNPs. The clinical testing implementation of a residual risk score in women at risk for hereditary breast cancer may offer significant potential for the management of greater than 90% of high-risk women who test negative for monogenic breast cancer mutations.

Example 2 Methods

Residual Risk Score (RRS) and Combined RRS (cRRS)

A polygenic RRS was developed and corrected for family history to determine breast cancer risk conferred by common genetic variants (see, e.g., Mavaddat et al. J Natl Cancer Inst. 2015; 107:djv036; Michalidou et al. Nat Genet. 2013; 45:353; Michailidou et al. Nat Genet. 2015; 47:373) independent of family history risk factors (FIG. 6). The cRRS was developed to account for genetic and family history risk factors by combining the RRS with the Tyrer-Cuzick model (Tyrer et al. Stat Med. 2004; 23:1111-30) (FIG. 6).

Cohorts

All patients had genetic testing for hereditary cancer risk and clinical information was obtained from provider-completed test request forms. Independent RRS training (N=24,259) and validation (N=10,575) cohorts were composed of women of European descent who had multi-gene panel testing and were negative for mutations in breast cancer risk genes (BRCA1, BRCA2, TP53, PTEN, STK11, CDH1, PALB2, CHEK2, ATM, NBN, BARD1). The cRRS was validated in a case-control cohort (N=1,617). Breast cancer cases had a first diagnosis of pathologically confirmed ductal invasive breast cancer within 1 year of multi-gene panel testing. Unaffected controls had genetic testing for hereditary non-polyposis colon cancer (HNPCC) and no cancer history of any type. The cRRS and Tyrer-Cuzick models were also evaluated in a large clinical cohort of unaffected individuals who had multi-gene panel testing between June 2017 and July 2017 (N=6,479).

RRS Development and Validation

TABLE B RRS Training and Validation Cohorts RRS Development Set RRS Validation Set All Patients BC Cases All Patients BC Cases (%) (%) (%) (%) Total Patients 24,259 (100) 4,291 (18) 10,575 (100) 1,627 (15) Age at Hereditary Cancer Testing Range 18-84 22-84 18-84 25-84 Median 47 54 46 54 % ≤ 50 61 7 63 6 Ancestry Western/Northern 18,079 (75) 3,443 (80) 7,717 (73) 1,283 (79) European Central/Eastern 5,533 (23) 762 (18) 2,548 (24) 310 (19) European Ashkenazi 647 (3) 86 (2) 309 (3) 34 (2) Cancer History in FDRs No BC or OC 13,230 (55) 2,800 (65) 5,889 (56) 1,044 (64) ≥1 BC 8,725 (36) 1,315 (31) 3,722 (35) 524 (32) ≥1 OC 2,978 (12) 251 (6) 1,247 (12) 80 (5) ≥2 BC 1,626 (7) 293 (7) 701 (7) 106 (7) ≥2 OC 104 (<1) 11 (<1) 45 (<1) 5 (<1) ≥l BC and ≥1 OC 674 (3) 75 (2) 283 (3) 21 (1) Abbreviations: FDR, First-degree relative; BC, Invasive ductal breast cancer; OC, Invasive epithelial ovarian cancer

Genotypes were determined with Next Generation Sequencing and validated with Sanger sequencing for 97 variants and single-nucleotide polymorphisms (SNPs) with reported associations with breast cancer risk. (See, e.g., Mavaddat et al. J Natl Cancer Inst. 2015; 107:djv036; Michalidou et al. Nat Genet. 2013; 45:353; Michailidou et al. Nat Genet. 2015; 47:373) SNP genotypes were coded as the number of the effect alleles (0, 1, or 2). SNP coefficients (β_(i)) were estimated using weighted averaging of log odds ratios from the training cohort and published studies. (Id.) Weights were inversely proportional to squares of confidence intervals. SNP “informativeness” was defined as 2f_(i)(1−f_(i))β_(i) ², where fi is the effect allele frequency for SNP_(i). It was determined that the 86 most informative SNPs (the SNPs in Panel 3) provided the optimal RRS (FIG. 7). The RRS was strongly associated with personal history of breast cancer in the validation cohort (p<10-31). Odds Ratio per unit standard deviation of the RRS: 1.41 (95% Cl=1.33-1.49).

cRRS Validation

TABLE C cRRS Validation Cohort All Patients BC Cases Unaffected (%) (%) Controls (%) Total Patients (1,617) (100) 990 (61) 627 (39) Age at Hereditary Cancer Testing Range 18-84 18-84 18-73 Median 48 50 44 % ≤ 50 57 52 67 Ancestry Ashkenazi Jewish 14 (1) 4 (0) 10 (2) White/Non-Hispanic 1,583 (98) 977 (99) 606 (97) Ashkenazi Jewish and 20 (1) 9 (1) 11 (2) White/Non-Hispanic Family Cancer History One or More FDR with BC 361 (22) 302 (31) 59 (9) FDR = First-degree relative; BC = Ductal invasive breast cancer.

TABLE D Association with Breast Cancer Odds Ratio* BC Risk Prediction Model (95% CI) P-Value Univariate Analysis CRRS Remaining Lifetime Risk 2.10 (1.85, 2.38) 4.1 × 10−35 Tyrer-Cuzick Remaining Lifetime Risk 1.85 (1.63, 2.09) 5.4 × 10−24 CRRS 5-Year Risk 4.64 (3.61, 5.95) 5.2 × 10−39 Tyrer-Cuzick 5-Year Risk 5.14 (3.78, 6.99) 3.5 × 10−28 Multivariate Analysis CRRS Remaining Lifetime Risk 2.00 (1.64, 2.43) 8.3 × 10−13 CRRS 5-Year Risk 3.91 (2.66, 5.75) 1.0 × 10−12 *Odds ratios and Wald Confidence Interval (CIs) are reported per standard deviation of the log odds of BC risk prediction models in unaffected controls. Log odds of CRRS remaining lifetime risk had SD 0.61; CRRS 5-year risk had SD 1.12.

The remaining lifetime and 5-year breast cancer risk estimates determined by cRRS and Tyrer-Cuzick were highly significant (Table D). cRRS (RRS+Tyrer-Cuzick) was more strongly associated with breast cancer than Tyrer-Cuzick alone. cRRS added significant breast cancer risk discrimination independent of that captured by Tyrer-Cuzick for both remaining lifetime risk (p=8.3×10−13) and 5-year risk (p=1.0×10−12) (Table D). Mean cRRS and Tyrer-Cuzick breast cancer risk estimates among unaffected controls were concordant, indicating that the cRRS was properly calibrated. The cRRS remaining lifetime risk estimates ranged from 0.88% to 66.4% in the clinical testing cohort (FIG. 8). 38.2% of patients had a lifetime risk >20% and 7.4% had a lifetime risk >35%. Such patients (i.e., with risk >20%) meet general guidelines, and are thus candidates, for increased screening for breast cancer (e.g., early and/or annual mammograms and/or breast MRI).

CONCLUSIONS

A residual risk score was developed and is highly predictive of risk of development of future breast cancer in unaffected women with significant family history after testing negative for known high and intermediate risk mutations. When the genetic risk from the residual risk score was combined with the Tyrer-Cuzick model, the resulting cRRS was a superior predictor of breast cancer risk compared to Tyrer-Cuzick alone. The clinical testing implementation of a combined residual risk score in women at risk for hereditary breast cancer may offer significant potential for the management of greater than 90% of high-risk women who test negative for monogenic mutations in breast cancer susceptibility genes.

All publications and patent applications are herein incorporated by reference to the same extent as if each individual publication or patent application was specifically and individually indicated to be incorporated by reference. The mere mentioning of the publications and patent applications does not necessarily constitute an admission that they are prior art to the instant application.

Although the foregoing disclosure has been described in some detail by way of illustration and example for purposes of clarity of understanding, it will be clear that certain changes and modifications may be practiced within the scope of the appended claims. 

1.-11. (canceled)
 12. A method comprising: genotyping each test loci in a panel of single nucleotide polymorphisms (SNPs) in a biological sample comprising DNA from a subject who has a familial history of breast cancer, wherein the subject has no pathogenic mutation in BRCA1 or BRCA2, and wherein the panel of SNPs comprises 86 SNPs selected from: rs10069690, rs13162653, rs2588809, rs6796502, rs1011970, rs132390, rs2736108, rs6828523, rs10472076, rs13267382, rs2823093, rs6964587, rs1053338, rs13281615, rs2943559, rs704010, rs10759243, rs13329835, rs2981579, rs7072776, rs10771399, rs13365225, rs3760982, rs720475, rs10941679, rs13387042, rs3803662, rs72755295, rs10995190, rs1353747, rs3817198, rs745570, rs11199914, rs1432679, rs3903072, rs75915166, rs11242675, rs1436904, rs4593472, rs7707921, rs11249433, rs1550623, rs4808801, rs7726159, rs11552449, rs16857609, rs4849887, rs78540526, rs11571833, rs17356907, rs4973768, rs7904519, rs11627032, rs17529111, rs527616, rs8170, rs11780156, rs17817449, rs554219, rs865686, rs11814448, rs17879961, rs6001930, rs889312, rs11820646, rs2012709, rs616488, rs941764, rs62070644, rs9693444, rs12493607, rs204247, rs6472903, rs9790517, rs12662670, rs2046210, rs6504950, rs999737, rs12710696, rs2236007, rs6507583, rs1292011, rs23639S6, and rs6762644.
 13. The method of claim 12, wherein the subject has no pathogenic mutation in CHEK2, ATM or TP53.
 14. The method of claim 12, wherein the subject has no pathogenic mutation in any of the following genes: BRCA1, BRCA2, CHEK2, ATM, NBN, PALB2, BARD1, BRIP1, PMS2, MSH2, MSH6, TP53 and MUYTH.
 15. The method of claim 12, wherein genotyping comprises next generation sequencing.
 16. The method of claim 15, wherein the next generation sequencing comprises sequencing genomic regions flanking each genomic locus.
 17. The method of claim 16, wherein the genomic regions flanking each genomic locus comprise at least 200 nucleotides on one or both sides of each genomic locus.
 18. The method of claim 15, wherein the next generation sequencing comprises pyrosequencing.
 19. The method of claim 12, wherein genotyping comprises a method selected from Polymerase Chain Reaction, (PCR), real-time PCR, allele-specific hybridization, strand displacement amplification (SDA), self-sustained sequence replication or single base extension.
 20. The method of claim 12, wherein genotyping comprises a method selected from agarose electrophoresis, polyacrylamide gel electrophoresis, capillary electrophoresis or denaturing gradient gel electrophoresis.
 21. The method of claim 12, wherein the panel of SNPs consists rs10069690, rs13162653, rs2588809, rs6796502, rs1011970, rs132390, rs2736108, rs6828523, rs10472076, rs13267382, rs2823093, rs6964587, rs1053338, rs13281615, rs2943559, rs704010, rs10759243, rs13329835, rs2981579, rs7072776, rs10771399, rs13365225, rs3760982, rs720475, rs10941679, rs13387042, rs3803662, rs72755295, rs10995190, rs1353747, rs3817198, rs745570, rs11199914, rs1432679, rs3903072, rs75915166, rs11242675, rs1436904, rs4593472, rs7707921, rs11249433, rs1550623, rs4808801, rs7726159, rs11552449, rs16857609, rs4849887, rs78540526, rs11571833, rs17356907, rs4973768, rs7904519, rs11627032, rs17529111, rs527616, rs8170, rs11780156, rs17817449, rs554219, rs865686, rs11814448, rs17879961, rs6001930, rs889312, rs11820646, rs2012709, rs616488, rs941764, rs62070644, rs9693444, rs12493607, rs204247, rs6472903, rs9790517, rs12662670, rs2046210, rs6504950, rs999737, rs12710696, rs2236007, rs6507583, rs1292011, rs23639S6, and rs6762644.
 22. A method of providing a breast cancer risk assessment to a human subject, comprising: obtaining a biological sample comprising DNA from a subject who has a familial history of breast cancer and who has no pathogenic mutation in BRCA1 or BRCA2; genotyping each test loci in a panel of single nucleotide polymorphisms (SNPs) comprising 86 SNPs selected from: rs10069690, rs13162653, rs2588809, rs6796502, rs1011970, rs132390, rs2736108, rs6828523, rs10472076, rs13267382, rs2823093, rs6964587, rs1053338, rs13281615, rs2943559, rs704010, rs10759243, rs13329835, rs2981579, rs7072776, rs10771399, rs13365225, rs3760982, rs720475, rs10941679, rs13387042, rs3803662, rs72755295, rs10995190, rs1353747, rs3817198, rs745570, rs11199914, rs1432679, rs3903072, rs75915166, rs11242675, rs1436904, rs4593472, rs7707921, rs11249433, rs1550623, rs4808801, rs7726159, rs11552449, rs16857609, rs4849887, rs78540526, rs11571833, rs17356907, rs4973768, rs7904519, rs11627032, rs17529111, rs527616, rs8170, rs11780156, rs17817449, rs554219, rs865686, rs11814448, rs17879961, rs6001930, rs889312, rs11820646, rs2012709, rs616488, rs941764, rs62070644, rs9693444, rs12493607, rs204247, rs6472903, rs9790517, rs12662670, rs2046210, rs6504950, rs999737, rs12710696, rs2236007, rs6507583, rs1292011, rs23639S6, and rs6762644; calculating a score of the subject's risk of developing breast cancer based on a variant allele ratio in the panel of SNPs; and providing to the subject the score of the subject's risk of developing breast cancer.
 23. The method of claim 22, wherein the score is a residual risk score (RRS), which is a sum of the product of number of effect alleles for each SNP (n_(i)) and a SNP coefficient for each SNP (βi) over the panel of SNPs.
 24. The method of claim 23, wherein the score is a combined residual risk score (cRRS) which accounts for RRS and additional genetic and family history risk factors.
 25. The method of claim 22, wherein the subject has no pathogenic mutation in any of the following genes: BRCA1, BRCA2, CHEK2, ATM, NBN, PALB2, BARD1, BRIP1, PMS2, MSH2, MSH6, TP53 and MUYTH.
 26. The method of claim 22, wherein genotyping comprises next generation sequencing.
 27. The method of claim 26, wherein the next generation sequencing comprises sequencing genomic regions flanking each genomic locus.
 28. The method of claim 27, wherein the genomic regions flanking each genomic locus comprise at least 200 nucleotides on one or both sides of each genomic locus.
 29. The method of claim 26, wherein the next generation sequencing comprises pyrosequencing.
 30. The method of claim 22, wherein genotyping comprises a method selected from Polymerase Chain Reaction, (PCR), real-time PCR, allele-specific hybridization, strand displacement amplification (SDA), self-sustained sequence replication or single base extension.
 31. The method of claim 22, wherein genotyping comprises a method selected from agarose electrophoresis, polyacrylamide gel electrophoresis, capillary electrophoresis or denaturing gradient gel electrophoresis. 